Package check result: NOTE Check: CRAN incoming feasibility, Result: NOTE Maintainer: ‘Terry M Therneau ’ Size of tarball: 8335513 bytes Changes to worse in reverse depends: Package: AER Check: examples New result: ERROR Running examples in ‘AER-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: PSID1976 > ### Title: Labor Force Participation Data > ### Aliases: PSID1976 > ### Keywords: datasets > > ### ** Examples > > ## Don't show: > if(!requireNamespace("sampleSelection")) { + if(interactive() || is.na(Sys.getenv("_R_CHECK_PACKAGE_NAME_", NA))) { + stop("not all packages required for the example are installed") + } else q() } > ## End(Don't show) > ## data and transformations > data("PSID1976") > PSID1976$kids <- with(PSID1976, factor((youngkids + oldkids) > 0, + levels = c(FALSE, TRUE), labels = c("no", "yes"))) > PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000) > PSID1976$partnum <- as.numeric(PSID1976$participation) - 1 > > ################### > ## Greene (2003) ## > ################### > > ## Example 4.1, Table 4.2 > ## (reproduced in Example 7.1, Table 7.1) > gr_lm <- lm(log(hours * wage) ~ age + I(age^2) + education + kids, + data = PSID1976, subset = participation == "yes") > summary(gr_lm) Call: lm(formula = log(hours * wage) ~ age + I(age^2) + education + kids, data = PSID1976, subset = participation == "yes") Residuals: Min 1Q Median 3Q Max -4.5305 -0.5266 0.3003 0.8474 1.7568 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.2400965 1.7674296 1.833 0.06747 . age 0.2005573 0.0838603 2.392 0.01721 * I(age^2) -0.0023147 0.0009869 -2.345 0.01947 * education 0.0674727 0.0252486 2.672 0.00782 ** kidsyes -0.3511952 0.1475326 -2.380 0.01773 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.19 on 423 degrees of freedom Multiple R-squared: 0.041, Adjusted R-squared: 0.03193 F-statistic: 4.521 on 4 and 423 DF, p-value: 0.001382 > vcov(gr_lm) (Intercept) age I(age^2) education kidsyes (Intercept) 3.12380756 -1.440901e-01 1.661740e-03 -9.260920e-03 2.674867e-02 age -0.14409007 7.032544e-03 -8.232369e-05 5.085495e-05 -2.641203e-03 I(age^2) 0.00166174 -8.232369e-05 9.739279e-07 -4.976114e-07 3.841018e-05 education -0.00926092 5.085495e-05 -4.976114e-07 6.374903e-04 -5.461931e-05 kidsyes 0.02674867 -2.641203e-03 3.841018e-05 -5.461931e-05 2.176587e-02 > > ## Example 4.5 > summary(gr_lm) Call: lm(formula = log(hours * wage) ~ age + I(age^2) + education + kids, data = PSID1976, subset = participation == "yes") Residuals: Min 1Q Median 3Q Max -4.5305 -0.5266 0.3003 0.8474 1.7568 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.2400965 1.7674296 1.833 0.06747 . age 0.2005573 0.0838603 2.392 0.01721 * I(age^2) -0.0023147 0.0009869 -2.345 0.01947 * education 0.0674727 0.0252486 2.672 0.00782 ** kidsyes -0.3511952 0.1475326 -2.380 0.01773 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.19 on 423 degrees of freedom Multiple R-squared: 0.041, Adjusted R-squared: 0.03193 F-statistic: 4.521 on 4 and 423 DF, p-value: 0.001382 > ## or equivalently > gr_lm1 <- lm(log(hours * wage) ~ 1, data = PSID1976, subset = participation == "yes") > anova(gr_lm1, gr_lm) Analysis of Variance Table Model 1: log(hours * wage) ~ 1 Model 2: log(hours * wage) ~ age + I(age^2) + education + kids Res.Df RSS Df Sum of Sq F Pr(>F) 1 427 625.08 2 423 599.46 4 25.625 4.5206 0.001382 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ## Example 21.4, p. 681, and Tab. 21.3, p. 682 > gr_probit1 <- glm(participation ~ age + I(age^2) + I(fincome/10000) + education + kids, + data = PSID1976, family = binomial(link = "probit") ) > gr_probit2 <- glm(participation ~ age + I(age^2) + I(fincome/10000) + education, + data = PSID1976, family = binomial(link = "probit")) > gr_probit3 <- glm(participation ~ kids/(age + I(age^2) + I(fincome/10000) + education), + data = PSID1976, family = binomial(link = "probit")) > ## LR test of all coefficients > lrtest(gr_probit1) Likelihood ratio test Model 1: participation ~ age + I(age^2) + I(fincome/10000) + education + kids Model 2: participation ~ 1 #Df LogLik Df Chisq Pr(>Chisq) 1 6 -490.85 2 1 -514.87 -5 48.051 3.468e-09 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## Chow-type test > lrtest(gr_probit2, gr_probit3) Likelihood ratio test Model 1: participation ~ age + I(age^2) + I(fincome/10000) + education Model 2: participation ~ kids/(age + I(age^2) + I(fincome/10000) + education) #Df LogLik Df Chisq Pr(>Chisq) 1 5 -496.87 2 10 -489.48 5 14.774 0.01137 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## equivalently: > anova(gr_probit2, gr_probit3, test = "Chisq") Analysis of Deviance Table Model 1: participation ~ age + I(age^2) + I(fincome/10000) + education Model 2: participation ~ kids/(age + I(age^2) + I(fincome/10000) + education) Resid. Df Resid. Dev Df Deviance Pr(>Chi) 1 748 993.73 2 743 978.96 5 14.774 0.01137 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > ## Table 21.3 > summary(gr_probit1) Call: glm(formula = participation ~ age + I(age^2) + I(fincome/10000) + education + kids, family = binomial(link = "probit"), data = PSID1976) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -4.1568189 1.4040095 -2.961 0.003070 ** age 0.1853957 0.0662076 2.800 0.005107 ** I(age^2) -0.0024259 0.0007762 -3.125 0.001775 ** I(fincome/10000) 0.0458029 0.0430557 1.064 0.287417 education 0.0981824 0.0228932 4.289 1.8e-05 *** kidsyes -0.4489872 0.1300252 -3.453 0.000554 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 1029.7 on 752 degrees of freedom Residual deviance: 981.7 on 747 degrees of freedom AIC: 993.7 Number of Fisher Scoring iterations: 4 > > ## Example 22.8, Table 22.7, p. 786 > library("sampleSelection") Loading required package: maxLik Loading required package: miscTools Please cite the 'maxLik' package as: Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1. If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site: https://r-forge.r-project.org/projects/maxlik/ > gr_2step <- selection(participation ~ age + I(age^2) + fincome + education + kids, + wage ~ experience + I(experience^2) + education + city, + data = PSID1976, method = "2step") > gr_ml <- selection(participation ~ age + I(age^2) + fincome + education + kids, + wage ~ experience + I(experience^2) + education + city, + data = PSID1976, method = "ml") > gr_ols <- lm(wage ~ experience + I(experience^2) + education + city, + data = PSID1976, subset = participation == "yes") > ## NOTE: ML estimates agree with Greene, 5e errata. > ## Standard errors are based on the Hessian (here), while Greene has BHHH/OPG. > > > ####################### > ## Wooldridge (2002) ## > ####################### > > ## Table 15.1, p. 468 > wl_lpm <- lm(partnum ~ nwincome + education + experience + I(experience^2) + + age + youngkids + oldkids, data = PSID1976) > wl_logit <- glm(participation ~ nwincome + education + experience + I(experience^2) + + age + youngkids + oldkids, family = binomial, data = PSID1976) > wl_probit <- glm(participation ~ nwincome + education + experience + I(experience^2) + + age + youngkids + oldkids, family = binomial(link = "probit"), data = PSID1976) > ## (same as Altman et al.) > > ## convenience functions > pseudoR2 <- function(obj) 1 - as.vector(logLik(obj)/logLik(update(obj, . ~ 1))) > misclass <- function(obj) 1 - sum(diag(prop.table(table( + model.response(model.frame(obj)), round(fitted(obj)))))) > > coeftest(wl_logit) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.4254524 0.8603645 0.4945 0.620951 nwincome -0.0213452 0.0084214 -2.5346 0.011256 * education 0.2211704 0.0434393 5.0915 3.553e-07 *** experience 0.2058695 0.0320567 6.4220 1.345e-10 *** I(experience^2) -0.0031541 0.0010161 -3.1041 0.001909 ** age -0.0880244 0.0145729 -6.0403 1.538e-09 *** youngkids -1.4433541 0.2035828 -7.0898 1.343e-12 *** oldkids 0.0601122 0.0747893 0.8038 0.421539 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > logLik(wl_logit) 'log Lik.' -401.7652 (df=8) > misclass(wl_logit) [1] 0.2642762 > pseudoR2(wl_logit) [1] 0.2196814 > > coeftest(wl_probit) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.27007357 0.50807817 0.5316 0.595031 nwincome -0.01202364 0.00493917 -2.4343 0.014919 * education 0.13090397 0.02539873 5.1540 2.550e-07 *** experience 0.12334717 0.01875869 6.5755 4.850e-11 *** I(experience^2) -0.00188707 0.00059993 -3.1455 0.001658 ** age -0.05285244 0.00846236 -6.2456 4.222e-10 *** youngkids -0.86832468 0.11837727 -7.3352 2.213e-13 *** oldkids 0.03600561 0.04403026 0.8177 0.413502 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > logLik(wl_probit) 'log Lik.' -401.3022 (df=8) > misclass(wl_probit) [1] 0.2656042 > pseudoR2(wl_probit) [1] 0.2205805 > > ## Table 16.2, p. 528 > form <- hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids > wl_ols <- lm(form, data = PSID1976) > wl_tobit <- tobit(form, data = PSID1976) > summary(wl_ols) Call: lm(formula = form, data = PSID1976) Residuals: Min 1Q Median 3Q Max -1511.3 -537.8 -146.9 538.1 3555.6 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1330.4824 270.7846 4.913 1.10e-06 *** nwincome -3.4466 2.5440 -1.355 0.1759 education 28.7611 12.9546 2.220 0.0267 * experience 65.6725 9.9630 6.592 8.23e-11 *** I(experience^2) -0.7005 0.3246 -2.158 0.0312 * age -30.5116 4.3639 -6.992 6.04e-12 *** youngkids -442.0899 58.8466 -7.513 1.66e-13 *** oldkids -32.7792 23.1762 -1.414 0.1577 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 750.2 on 745 degrees of freedom Multiple R-squared: 0.2656, Adjusted R-squared: 0.2587 F-statistic: 38.5 on 7 and 745 DF, p-value: < 2.2e-16 > summary(wl_tobit) Call: tobit(formula = hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids, data = PSID1976) Observations: Total Left-censored Uncensored Right-censored 753 325 428 0 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 965.30528 446.43614 2.162 0.030599 * nwincome -8.81424 4.45910 -1.977 0.048077 * education 80.64561 21.58324 3.736 0.000187 *** experience 131.56430 17.27939 7.614 2.66e-14 *** I(experience^2) -1.86416 0.53766 -3.467 0.000526 *** age -54.40501 7.41850 -7.334 2.24e-13 *** youngkids -894.02174 111.87804 -7.991 1.34e-15 *** oldkids -16.21800 38.64139 -0.420 0.674701 Log(scale) 7.02289 0.03706 189.514 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Scale: 1122 Gaussian distribution Number of Newton-Raphson Iterations: 4 Log-likelihood: -3819 on 9 Df Wald-statistic: 253.9 on 7 Df, p-value: < 2.22e-16 > > > ####################### > ## McCullough (2004) ## > ####################### > > ## p. 203 > mc_probit <- glm(participation ~ nwincome + education + experience + I(experience^2) + + age + youngkids + oldkids, family = binomial(link = "probit"), data = PSID1976) > mc_tobit <- tobit(hours ~ nwincome + education + experience + I(experience^2) + age + + youngkids + oldkids, data = PSID1976) > coeftest(mc_probit) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.27007357 0.50807817 0.5316 0.595031 nwincome -0.01202364 0.00493917 -2.4343 0.014919 * education 0.13090397 0.02539873 5.1540 2.550e-07 *** experience 0.12334717 0.01875869 6.5755 4.850e-11 *** I(experience^2) -0.00188707 0.00059993 -3.1455 0.001658 ** age -0.05285244 0.00846236 -6.2456 4.222e-10 *** youngkids -0.86832468 0.11837727 -7.3352 2.213e-13 *** oldkids 0.03600561 0.04403026 0.8177 0.413502 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > coeftest(mc_tobit) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 965.305283 446.436144 2.1622 0.0305991 * nwincome -8.814243 4.459100 -1.9767 0.0480771 * education 80.645606 21.583237 3.7365 0.0001866 *** experience 131.564299 17.279392 7.6139 2.659e-14 *** I(experience^2) -1.864158 0.537662 -3.4672 0.0005260 *** age -54.405011 7.418502 -7.3337 2.239e-13 *** youngkids -894.021739 111.878035 -7.9910 1.338e-15 *** oldkids -16.217996 38.641391 -0.4197 0.6747008 Log(scale) 7.022887 0.037057 189.5142 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > coeftest(mc_tobit, vcov = vcovOPG) Error in model.matrix.default(terms(x), mf) : model frame and formula mismatch in model.matrix() Calls: coeftest ... estfun.survreg -> model.matrix -> model.matrix.default Execution halted Package: AER Check: tests New result: ERROR Running ‘Ch-Basics.R’ [2s/2s] Comparing ‘Ch-Basics.Rout’ to ‘Ch-Basics.Rout.save’ ... OK Running ‘Ch-Intro.R’ [2s/2s] Comparing ‘Ch-Intro.Rout’ to ‘Ch-Intro.Rout.save’ ... OK Running ‘Ch-LinearRegression.R’ [5s/5s] Comparing ‘Ch-LinearRegression.Rout’ to ‘Ch-LinearRegression.Rout.save’ ... OK Running ‘Ch-Microeconometrics.R’ [3s/3s] Running ‘Ch-Programming.R’ [24s/25s] Comparing ‘Ch-Programming.Rout’ to ‘Ch-Programming.Rout.save’ ... OK Running ‘Ch-TimeSeries.R’ [4s/4s] Comparing ‘Ch-TimeSeries.Rout’ to ‘Ch-TimeSeries.Rout.save’ ... OK Running ‘Ch-Validation.R’ [46s/46s] Comparing ‘Ch-Validation.Rout’ to ‘Ch-Validation.Rout.save’ ... OK Running the tests in ‘tests/Ch-Microeconometrics.R’ failed. Complete output: > if(!requireNamespace("ROCR") || + !requireNamespace("MASS") || + !requireNamespace("pscl") || + !requireNamespace("np") || + !requireNamespace("nnet")) q() Loading required namespace: ROCR Loading required namespace: MASS Loading required namespace: pscl Loading required namespace: np Loading required namespace: nnet > > ################################################### > ### chunk number 1: setup > ################################################### > options(prompt = "R> ", continue = "+ ", width = 64, + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE) R> R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))}, + twofig = function() {par(mfrow = c(1,2))}, + threefig = function() {par(mfrow = c(1,3))}, + fourfig = function() {par(mfrow = c(2,2))}, + sixfig = function() {par(mfrow = c(3,2))})) R> R> library("AER") Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival R> R> suppressWarnings(RNGversion("3.5.0")) R> set.seed(1071) R> R> R> ################################################### R> ### chunk number 2: swisslabor-data R> ################################################### R> data("SwissLabor") R> swiss_probit <- glm(participation ~ . + I(age^2), + data = SwissLabor, family = binomial(link = "probit")) R> summary(swiss_probit) Call: glm(formula = participation ~ . + I(age^2), family = binomial(link = "probit"), data = SwissLabor) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.7491 1.4069 2.66 0.0077 income -0.6669 0.1320 -5.05 4.3e-07 age 2.0753 0.4054 5.12 3.1e-07 education 0.0192 0.0179 1.07 0.2843 youngkids -0.7145 0.1004 -7.12 1.1e-12 oldkids -0.1470 0.0509 -2.89 0.0039 foreignyes 0.7144 0.1213 5.89 3.9e-09 I(age^2) -0.2943 0.0499 -5.89 3.8e-09 (Dispersion parameter for binomial family taken to be 1) Null deviance: 1203.2 on 871 degrees of freedom Residual deviance: 1017.2 on 864 degrees of freedom AIC: 1033 Number of Fisher Scoring iterations: 4 R> R> R> ################################################### R> ### chunk number 3: swisslabor-plot eval=FALSE R> ################################################### R> ## plot(participation ~ age, data = SwissLabor, ylevels = 2:1) R> R> R> ################################################### R> ### chunk number 4: swisslabor-plot-refined R> ################################################### R> plot(participation ~ education, data = SwissLabor, ylevels = 2:1) R> fm <- glm(participation ~ education + I(education^2), data = SwissLabor, family = binomial) R> edu <- sort(unique(SwissLabor$education)) R> prop <- sapply(edu, function(x) mean(SwissLabor$education <= x)) R> lines(predict(fm, newdata = data.frame(education = edu), type = "response") ~ prop, col = 2) R> R> plot(participation ~ age, data = SwissLabor, ylevels = 2:1) R> fm <- glm(participation ~ age + I(age^2), data = SwissLabor, family = binomial) R> ag <- sort(unique(SwissLabor$age)) R> prop <- sapply(ag, function(x) mean(SwissLabor$age <= x)) R> lines(predict(fm, newdata = data.frame(age = ag), type = "response") ~ prop, col = 2) R> R> R> ################################################### R> ### chunk number 5: effects1 R> ################################################### R> fav <- mean(dnorm(predict(swiss_probit, type = "link"))) R> fav * coef(swiss_probit) (Intercept) income age education youngkids 1.241930 -0.220932 0.687466 0.006359 -0.236682 oldkids foreignyes I(age^2) -0.048690 0.236644 -0.097505 R> R> R> ################################################### R> ### chunk number 6: effects2 R> ################################################### R> av <- colMeans(SwissLabor[, -c(1, 7)]) R> av <- data.frame(rbind(swiss = av, foreign = av), + foreign = factor(c("no", "yes"))) R> av <- predict(swiss_probit, newdata = av, type = "link") R> av <- dnorm(av) R> av["swiss"] * coef(swiss_probit)[-7] (Intercept) income age education youngkids 1.495137 -0.265976 0.827628 0.007655 -0.284938 oldkids I(age^2) -0.058617 -0.117384 R> R> R> ################################################### R> ### chunk number 7: effects3 R> ################################################### R> av["foreign"] * coef(swiss_probit)[-7] (Intercept) income age education youngkids 1.136517 -0.202180 0.629115 0.005819 -0.216593 oldkids I(age^2) -0.044557 -0.089229 R> R> R> ################################################### R> ### chunk number 8: mcfadden R> ################################################### R> swiss_probit0 <- update(swiss_probit, formula = . ~ 1) R> 1 - as.vector(logLik(swiss_probit)/logLik(swiss_probit0)) [1] 0.1546 R> R> R> ################################################### R> ### chunk number 9: confusion-matrix R> ################################################### R> table(true = SwissLabor$participation, + pred = round(fitted(swiss_probit))) pred true 0 1 no 337 134 yes 146 255 R> R> R> ################################################### R> ### chunk number 10: confusion-matrix1 R> ################################################### R> tab <- table(true = SwissLabor$participation, + pred = round(fitted(swiss_probit))) R> tabp <- round(100 * c(tab[1,1] + tab[2,2], tab[2,1] + tab[1,2])/sum(tab), digits = 2) R> R> R> ################################################### R> ### chunk number 11: roc-plot eval=FALSE R> ################################################### R> ## library("ROCR") R> ## pred <- prediction(fitted(swiss_probit), R> ## SwissLabor$participation) R> ## plot(performance(pred, "acc")) R> ## plot(performance(pred, "tpr", "fpr")) R> ## abline(0, 1, lty = 2) R> R> R> ################################################### R> ### chunk number 12: roc-plot1 R> ################################################### R> library("ROCR") R> pred <- prediction(fitted(swiss_probit), + SwissLabor$participation) R> plot(performance(pred, "acc")) R> plot(performance(pred, "tpr", "fpr")) R> abline(0, 1, lty = 2) R> R> R> ################################################### R> ### chunk number 13: rss R> ################################################### R> deviance(swiss_probit) [1] 1017 R> sum(residuals(swiss_probit, type = "deviance")^2) [1] 1017 R> sum(residuals(swiss_probit, type = "pearson")^2) [1] 866.5 R> R> R> ################################################### R> ### chunk number 14: coeftest eval=FALSE R> ################################################### R> ## coeftest(swiss_probit, vcov = sandwich) R> R> R> ################################################### R> ### chunk number 15: murder R> ################################################### R> data("MurderRates") R> ## murder_logit <- glm(I(executions > 0) ~ time + income + ## IGNORE_RDIFF, excluded due to small numeric deviations on different platforms R> ## noncauc + lfp + southern, data = MurderRates, R> ## family = binomial) R> ## R> ## R> ## ################################################### R> ## ### chunk number 16: murder-coeftest R> ## ################################################### R> ## coeftest(murder_logit) R> ## R> ## R> ## ################################################### R> ## ### chunk number 17: murder2 R> ## ################################################### R> ## murder_logit2 <- glm(I(executions > 0) ~ time + income + R> ## noncauc + lfp + southern, data = MurderRates, R> ## family = binomial, control = list(epsilon = 1e-15, R> ## maxit = 50, trace = FALSE)) R> ## R> ## R> ## ################################################### R> ## ### chunk number 18: murder2-coeftest R> ## ################################################### R> ## coeftest(murder_logit2) R> R> R> ################################################### R> ### chunk number 19: separation R> ################################################### R> table(I(MurderRates$executions > 0), MurderRates$southern) no yes FALSE 9 0 TRUE 20 15 R> R> R> ################################################### R> ### chunk number 20: countreg-pois R> ################################################### R> data("RecreationDemand") R> rd_pois <- glm(trips ~ ., data = RecreationDemand, + family = poisson) R> R> R> ################################################### R> ### chunk number 21: countreg-pois-coeftest R> ################################################### R> coeftest(rd_pois) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.26499 0.09372 2.83 0.0047 quality 0.47173 0.01709 27.60 < 2e-16 skiyes 0.41821 0.05719 7.31 2.6e-13 income -0.11132 0.01959 -5.68 1.3e-08 userfeeyes 0.89817 0.07899 11.37 < 2e-16 costC -0.00343 0.00312 -1.10 0.2713 costS -0.04254 0.00167 -25.47 < 2e-16 costH 0.03613 0.00271 13.34 < 2e-16 R> R> R> ################################################### R> ### chunk number 22: countreg-pois-logLik R> ################################################### R> logLik(rd_pois) 'log Lik.' -1529 (df=8) R> R> R> ################################################### R> ### chunk number 23: countreg-odtest1 R> ################################################### R> dispersiontest(rd_pois) Overdispersion test data: rd_pois z = 2.4, p-value = 0.008 alternative hypothesis: true dispersion is greater than 1 sample estimates: dispersion 6.566 R> R> R> ################################################### R> ### chunk number 24: countreg-odtest2 R> ################################################### R> dispersiontest(rd_pois, trafo = 2) Overdispersion test data: rd_pois z = 2.9, p-value = 0.002 alternative hypothesis: true alpha is greater than 0 sample estimates: alpha 1.316 R> R> R> ################################################### R> ### chunk number 25: countreg-nbin R> ################################################### R> library("MASS") R> rd_nb <- glm.nb(trips ~ ., data = RecreationDemand) R> coeftest(rd_nb) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.12194 0.21430 -5.24 1.6e-07 quality 0.72200 0.04012 18.00 < 2e-16 skiyes 0.61214 0.15030 4.07 4.6e-05 income -0.02606 0.04245 -0.61 0.539 userfeeyes 0.66917 0.35302 1.90 0.058 costC 0.04801 0.00918 5.23 1.7e-07 costS -0.09269 0.00665 -13.93 < 2e-16 costH 0.03884 0.00775 5.01 5.4e-07 R> logLik(rd_nb) 'log Lik.' -825.6 (df=9) R> R> R> ################################################### R> ### chunk number 26: countreg-se R> ################################################### R> round(sqrt(rbind(diag(vcov(rd_pois)), + diag(sandwich(rd_pois)))), digits = 3) (Intercept) quality skiyes income userfeeyes costC costS [1,] 0.094 0.017 0.057 0.02 0.079 0.003 0.002 [2,] 0.432 0.049 0.194 0.05 0.247 0.015 0.012 costH [1,] 0.003 [2,] 0.009 R> R> R> ################################################### R> ### chunk number 27: countreg-sandwich R> ################################################### R> coeftest(rd_pois, vcov = sandwich) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.26499 0.43248 0.61 0.54006 quality 0.47173 0.04885 9.66 < 2e-16 skiyes 0.41821 0.19387 2.16 0.03099 income -0.11132 0.05031 -2.21 0.02691 userfeeyes 0.89817 0.24691 3.64 0.00028 costC -0.00343 0.01470 -0.23 0.81549 costS -0.04254 0.01173 -3.62 0.00029 costH 0.03613 0.00939 3.85 0.00012 R> R> R> ################################################### R> ### chunk number 28: countreg-OPG R> ################################################### R> round(sqrt(diag(vcovOPG(rd_pois))), 3) (Intercept) quality skiyes income userfeeyes 0.025 0.007 0.020 0.010 0.033 costC costS costH 0.001 0.000 0.001 R> R> R> ################################################### R> ### chunk number 29: countreg-plot R> ################################################### R> plot(table(RecreationDemand$trips), ylab = "") R> R> R> ################################################### R> ### chunk number 30: countreg-zeros R> ################################################### R> rbind(obs = table(RecreationDemand$trips)[1:10], exp = round( + sapply(0:9, function(x) sum(dpois(x, fitted(rd_pois)))))) 0 1 2 3 4 5 6 7 8 9 obs 417 68 38 34 17 13 11 2 8 1 exp 277 146 68 41 30 23 17 13 10 7 R> R> R> ################################################### R> ### chunk number 31: countreg-pscl R> ################################################### R> library("pscl") Classes and Methods for R originally developed in the Political Science Computational Laboratory Department of Political Science Stanford University (2002-2015), by and under the direction of Simon Jackman. hurdle and zeroinfl functions by Achim Zeileis. R> R> R> ################################################### R> ### chunk number 32: countreg-zinb R> ################################################### R> rd_zinb <- zeroinfl(trips ~ . | quality + income, + data = RecreationDemand, dist = "negbin") R> R> R> ################################################### R> ### chunk number 33: countreg-zinb-summary R> ################################################### R> summary(rd_zinb) Call: zeroinfl(formula = trips ~ . | quality + income, data = RecreationDemand, dist = "negbin") Pearson residuals: Min 1Q Median 3Q Max -1.0889 -0.2004 -0.0570 -0.0451 40.0139 Count model coefficients (negbin with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 1.09663 0.25668 4.27 1.9e-05 quality 0.16891 0.05303 3.19 0.0014 skiyes 0.50069 0.13449 3.72 0.0002 income -0.06927 0.04380 -1.58 0.1138 userfeeyes 0.54279 0.28280 1.92 0.0549 costC 0.04044 0.01452 2.79 0.0053 costS -0.06621 0.00775 -8.55 < 2e-16 costH 0.02060 0.01023 2.01 0.0441 Log(theta) 0.19017 0.11299 1.68 0.0924 Zero-inflation model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) 5.743 1.556 3.69 0.00022 quality -8.307 3.682 -2.26 0.02404 income -0.258 0.282 -0.92 0.35950 Theta = 1.209 Number of iterations in BFGS optimization: 26 Log-likelihood: -722 on 12 Df R> R> R> ################################################### R> ### chunk number 34: countreg-zinb-expected R> ################################################### R> round(colSums(predict(rd_zinb, type = "prob")[,1:10])) 0 1 2 3 4 5 6 7 8 9 433 47 35 27 20 16 12 10 8 7 R> R> R> ################################################### R> ### chunk number 35: countreg-hurdle R> ################################################### R> rd_hurdle <- hurdle(trips ~ . | quality + income, + data = RecreationDemand, dist = "negbin") R> summary(rd_hurdle) Call: hurdle(formula = trips ~ . | quality + income, data = RecreationDemand, dist = "negbin") Pearson residuals: Min 1Q Median 3Q Max -1.610 -0.207 -0.185 -0.164 12.111 Count model coefficients (truncated negbin with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 0.8419 0.3828 2.20 0.0278 quality 0.1717 0.0723 2.37 0.0176 skiyes 0.6224 0.1901 3.27 0.0011 income -0.0571 0.0645 -0.88 0.3763 userfeeyes 0.5763 0.3851 1.50 0.1345 costC 0.0571 0.0217 2.63 0.0085 costS -0.0775 0.0115 -6.71 1.9e-11 costH 0.0124 0.0149 0.83 0.4064 Log(theta) -0.5303 0.2611 -2.03 0.0423 Zero hurdle model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) -2.7663 0.3623 -7.64 2.3e-14 quality 1.5029 0.1003 14.98 < 2e-16 income -0.0447 0.0785 -0.57 0.57 Theta: count = 0.588 Number of iterations in BFGS optimization: 18 Log-likelihood: -765 on 12 Df R> R> R> ################################################### R> ### chunk number 36: countreg-hurdle-expected R> ################################################### R> round(colSums(predict(rd_hurdle, type = "prob")[,1:10])) 0 1 2 3 4 5 6 7 8 9 417 74 42 27 19 14 10 8 6 5 R> R> R> ################################################### R> ### chunk number 37: tobit1 R> ################################################### R> data("Affairs") R> aff_tob <- tobit(affairs ~ age + yearsmarried + + religiousness + occupation + rating, data = Affairs) R> summary(aff_tob) Call: tobit(formula = affairs ~ age + yearsmarried + religiousness + occupation + rating, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 8.1742 2.7414 2.98 0.0029 age -0.1793 0.0791 -2.27 0.0234 yearsmarried 0.5541 0.1345 4.12 3.8e-05 religiousness -1.6862 0.4038 -4.18 3.0e-05 occupation 0.3261 0.2544 1.28 0.2000 rating -2.2850 0.4078 -5.60 2.1e-08 Log(scale) 2.1099 0.0671 31.44 < 2e-16 Scale: 8.25 Gaussian distribution Number of Newton-Raphson Iterations: 4 Log-likelihood: -706 on 7 Df Wald-statistic: 67.7 on 5 Df, p-value: 3.1e-13 R> R> R> ################################################### R> ### chunk number 38: tobit2 R> ################################################### R> aff_tob2 <- update(aff_tob, right = 4) R> summary(aff_tob2) Call: tobit(formula = affairs ~ age + yearsmarried + religiousness + occupation + rating, right = 4, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 451 70 80 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 7.9010 2.8039 2.82 0.00483 age -0.1776 0.0799 -2.22 0.02624 yearsmarried 0.5323 0.1412 3.77 0.00016 religiousness -1.6163 0.4244 -3.81 0.00014 occupation 0.3242 0.2539 1.28 0.20162 rating -2.2070 0.4498 -4.91 9.3e-07 Log(scale) 2.0723 0.1104 18.77 < 2e-16 Scale: 7.94 Gaussian distribution Number of Newton-Raphson Iterations: 4 Log-likelihood: -500 on 7 Df Wald-statistic: 42.6 on 5 Df, p-value: 4.5e-08 R> R> R> ################################################### R> ### chunk number 39: tobit3 R> ################################################### R> linearHypothesis(aff_tob, c("age = 0", "occupation = 0"), + vcov = sandwich) Error in model.matrix.default(terms(x), mf) : model frame and formula mismatch in model.matrix() Calls: linearHypothesis ... estfun.survreg -> model.matrix -> model.matrix.default Execution halted Package: butcher Check: tests New result: ERROR Running ‘testthat.R’ [15s/14s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(butcher) > > test_check("butcher") [ FAIL 1 | WARN 0 | SKIP 35 | PASS 219 ] ══ Skipped tests (35) ══════════════════════════════════════════════════════════ • On CRAN (34): 'test-c5.R:2:3', 'test-earth.R:2:3', 'test-earth.R:31:3', 'test-elnet.R:2:3', 'test-flexsurvreg.R:2:3', 'test-flexsurvreg.R:15:3', 'test-flexsurvreg.R:43:3', 'test-gausspr.R:2:3', 'test-glmnet.R:2:3', 'test-kknn.R:2:3', 'test-klaR.R:2:3', 'test-klaR.R:17:3', 'test-ksvm.R:2:3', 'test-mda.R:2:3', 'test-mda.R:23:3', 'test-mda.R:95:3', 'test-mixOmics.R:2:3', 'test-mixOmics.R:21:3', 'test-mixOmics.R:38:3', 'test-multnet.R:2:3', 'test-nnet.R:2:3', 'test-randomForest.R:2:3', 'test-rpart.R:2:3', 'test-rpart.R:18:3', 'test-sclass.R:2:3', 'test-survreg.R:2:3', 'test-survreg.penal.R:2:3', 'test-train.R:2:3', 'test-train.R:42:3', 'test-train.recipe.R:9:3', 'test-ui.R:9:3', 'test-weigh.R:12:3', 'test-xgb.R:6:3', 'test-xgb.R:34:3' • {mixOmics} is not installed (1): 'test-recipe.R:419:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-ipred.R:116:3'): ipred + rpart + predict() works (survbagg) ── predict(x, DLBCL[1:2, ]) (`actual`) not equal to predict(fit, DLBCL[1:2, ]) (`expected`). `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$.Class, 'previous')` is a character vector ('butchered_survbagg', 'survbagg') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$.Class, 'previous')` is absent class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$Terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$Terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$Terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$Terms, '.Environment')` is class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object) - "butchered_survbagg" "survbagg" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object, 'butcher_disabled')` is a character vector ('print()', 'summary()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object, 'butcher_disabled')` is absent class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(39.6, ` + `88.1, 68.8, 69.6, 51.2, 2.9, 4.6, 68.8, 5.1, 3.4, 89.8, 27.1, ` + `12.3, 8.2, 69.1, 88.1, 129.9, 5.1, 102.4, 31.47, 31.47, 8.3, ` + `59, 15.5, 1.3, 77.4, 72.03, 4.6, 23.7, 1.3, 102.4, 27.1, 27.1, ` + `31.47, 91.33, 5.1, 56.6, 39.6, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, ` + `0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, ` + `1, 1, 1, 0, 1, 0, 1), dim = c(38L, 2L), dimnames = list(NULL, ` + ` c("time", "status")), type = "right", class = "Surv"), MGEc.1 = c(0.259723583341476, ` + `0.375292445076398, -0.149830661743812, 0.12559682847857, -0.471191138855285, ` + `0.330829257500481, -0.169338484929163, -0.149830661743812, -0.239593761387222, ` and 125 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[1]]$btree$y + 0.735500906572494 + 0.857161511112817 + 0.808748115903987 + 0.810754888762385 + 0.764599113019251 + 0.0804093567251462 + 0.167804314708339 + 0.808748115903987 + 0.261554314708339 + 0.108980785296575 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(31.47, ` + `71.3, 4.6, 8.2, 31.47, 5.1, 129.9, 31.47, 80.4, 129.9, 15.5, ` + `3.2, 9.45, 23.7, 90.2, 4.6, 88.1, 51.2, 4.6, 69.1, 80.4, 9.45, ` + `88.1, 9.45, 71.3, 51.2, 2.9, 3.2, 12.3, 12.3, 69.1, 8.3, 3.4, ` + `4.1, 23.7, 15.5, 91.33, 11.8, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, ` + `1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, ` + `1, 1, 1, 1, 0, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(-0.16793386592959, ` + `-0.434279176291204, -0.169338484929163, -0.148203548115746, -0.16793386592959, ` + `-0.239593761387222, 0.486528445998275, -0.16793386592959, 0.428895573591471, ` and 108 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[2]]$btree$y + 0.949498646267483 + 1.00474519595886 + 0.22926212771414 + 0.297078219668163 + 0.949498646267483 + 0.262595461047473 + 1.08602683823641 + 0.949498646267483 + 1.0173674305105 + 1.08602683823641 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(91.33, ` + `51.2, 8.2, 8.2, 23.7, 9.45, 23.7, 51.2, 77.4, 23.7, 69.1, 4.1, ` + `4.6, 91.33, 3.4, 8.3, 23.7, 69.6, 90.2, 59, 4.1, 9.45, 1.3, 90.2, ` + `68.8, 69.6, 39.6, 69.1, 90.2, 3.4, 11.8, 22.3, 23.7, 15.5, 12.3, ` + `59, 4.6, 69.1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, ` + `1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, ` + `0), dim = c(38L, 2L), dimnames = list(NULL, c("time", "status"` + `)), type = "right", class = "Surv"), MGEc.1 = c(0.678449318884034, ` + `-0.471191138855285, -0.148203548115746, -0.148203548115746, -0.0322368514978663, ` + `-0.498833871076256, -0.0322368514978663, -0.471191138855285, ` and 131 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[3]]$btree$y + 0.843640789814761 + 0.793669430265039 + 0.262634890308914 + 0.262634890308914 + 0.759425413628673 + 0.368546220358175 + 0.759425413628673 + 0.793669430265039 + 0.826294638842231 + 0.759425413628673 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(2.4, ` + `56.6, 31.47, 11.8, 72.03, 129.9, 89.8, 9.45, 22.3, 69.6, 39.6, ` + `4.6, 59, 91.33, 69.1, 80.4, 9.45, 1.3, 129.9, 4.6, 69.6, 3.4, ` + `2.4, 15.5, 3.2, 1.3, 53.73, 8.2, 53.73, 4.6, 5.1, 9.45, 8.2, ` + `23.7, 69.6, 69.1, 59, 3.2, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, ` + `0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, ` + `1, 0, 0, 0, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(-0.576390672497128, ` + `-0.155361716202472, -0.16793386592959, 0.547713434723109, 0.667863127862309, ` + `0.486528445998275, -0.110029799156235, -0.498833871076256, -0.617520610210315, ` and 108 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[4]]$btree$y + 0.108187134502924 + 0.81064923099221 + 0.776083944827953 + 0.570277762705981 + 0.831872564279695 + 0.911470380050708 + 0.856314471766318 + 0.524823217251436 + 0.667896810325029 + 0.828530198765204 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(39.6, ` + `5.1, 71.3, 39.6, 69.6, 11.8, 31.47, 56.6, 31.47, 129.9, 88.1, ` + `56.6, 9.45, 12.3, 89.8, 69.6, 89.8, 53.73, 4.1, 88.1, 88.1, 1.3, ` + `91.33, 68.8, 77.4, 72.03, 129.9, 102.4, 12.3, 22.3, 4.6, 39.6, ` + `23.7, 1.3, 12.3, 69.6, 89.8, 88.1, 1, 1, 1, 1, 0, 1, 1, 0, 1, ` + `0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, ` + `1, 1, 1, 1, 1, 0, 0, 0), dim = c(38L, 2L), dimnames = list(NULL, ` + ` c("time", "status")), type = "right", class = "Surv"), MGEc.1 = c(0.259723583341476, ` + `-0.239593761387222, -0.244315812326253, 0.259723583341476, 0.12559682847857, ` + `0.547713434723109, -0.16793386592959, -0.155361716202472, -0.16793386592959, ` and 108 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[5]]$btree$y + 0.571394173528274 + 0.138392550002457 + 0.606268044527955 + 0.571394173528274 + 0.604397836934912 + 0.199945580305487 + 0.446394173528274 + 0.590096249458702 + 0.446394173528274 + 0.670735200382255 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(8.3, ` + `71.3, 129.9, 68.8, 23.7, 80.4, 8.2, 3.2, 8.2, 8.3, 39.6, 56.6, ` + `2.9, 2.4, 4.1, 12.3, 22.3, 53.73, 3.4, 3.4, 3.4, 3.2, 69.6, 56.6, ` + `22.3, 12.3, 77.4, 15.5, 69.6, 83.8, 90.2, 12.3, 71.3, 9.45, 23.7, ` + `3.4, 83.8, 15.5, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, ` + `1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, ` + `0, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", "status"` + `)), type = "right", class = "Surv"), MGEc.1 = c(-0.671223095093067, ` + `-0.244315812326253, 0.486528445998275, -0.149830661743812, -0.0322368514978663, ` + `0.428895573591471, -0.148203548115746, -0.0364146689892928, -0.148203548115746, ` and 117 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[6]]$btree$y + 0.402918355528583 + 1.01505761702914 + 1.13026906739582 + 1.01014245276436 + 0.886066667484944 + 1.03294881495298 + 0.328844281454509 + 0.108898372056267 + 0.328844281454509 + 0.402918355528583 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(4.6, ` + `53.73, 9.45, 71.3, 8.3, 102.4, 59, 91.33, 77.4, 3.2, 39.6, 90.2, ` + `69.1, 80.4, 15.5, 3.4, 68.8, 11.8, 91.33, 5.1, 3.2, 69.6, 83.8, ` + `22.3, 8.2, 8.2, 23.7, 71.3, 69.1, 71.3, 88.1, 102.4, 91.33, 72.03, ` + `102.4, 2.4, 2.9, 4.1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, ` + `0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, ` + `0, 1, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(-0.169338484929163, ` + `0.0480491126589312, -0.498833871076256, -0.244315812326253, -0.671223095093067, ` + `0.0255708109007852, -0.474079791435018, 0.678449318884034, -0.0263613327352018, ` and 111 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[7]]$btree$y + 0.199863167065179 + 0.614053916973662 + 0.371539220999298 + 0.655652022922167 + 0.334502183962261 + 0.729283274771827 + 0.626530981193845 + 0.703074337216337 + 0.67009416556792 + 0.108898372056267 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(88.1, ` + `31.47, 3.4, 2.4, 11.8, 2.4, 72.03, 31.47, 27.1, 5.1, 3.2, 102.4, ` + `5.1, 9.45, 4.6, 59, 102.4, 83.8, 11.8, 68.8, 71.3, 88.1, 88.1, ` + `77.4, 69.6, 3.4, 89.8, 8.3, 3.4, 90.2, 69.1, 8.3, 8.3, 3.2, 53.73, ` + `31.47, 1.3, 12.3, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, ` + `0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, ` + `1, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", "status"` + `)), type = "right", class = "Surv"), MGEc.1 = c(0.375292445076398, ` + `-0.16793386592959, -0.257859001561977, -0.576390672497128, 0.547713434723109, ` + `-0.576390672497128, 0.667863127862309, -0.16793386592959, 0.000227688335335926, ` and 128 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[8]]$btree$y + 0.899481720394734 + 0.82596872613246 + 0.228421791579686 + 0.0803698435277383 + 0.570454941671307 + 0.0803698435277383 + 0.878620801839291 + 0.82596872613246 + 0.668073989290355 + 0.330720642154399 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(91.33, ` + `90.2, 91.33, 22.3, 5.1, 90.2, 83.8, 39.6, 56.6, 39.6, 4.1, 39.6, ` + `12.3, 72.03, 8.2, 31.47, 5.1, 51.2, 51.2, 4.1, 9.45, 91.33, 4.1, ` + `4.1, 12.3, 8.3, 89.8, 9.45, 77.4, 2.9, 69.1, 80.4, 69.6, 68.8, ` + `4.1, 3.2, 1.3, 3.4, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, ` + `1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, ` + `1, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", "status"` + `)), type = "right", class = "Surv"), MGEc.1 = c(0.678449318884034, ` + `0.0706053420547389, 0.678449318884034, -0.617520610210315, -0.239593761387222, ` + `0.0706053420547389, -0.0360203071525677, 0.259723583341476, -0.155361716202472, ` and 124 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[9]]$btree$y + 0.906776028948751 + 0.90222685357066 + 0.906776028948751 + 0.615790508477462 + 0.325716363620709 + 0.90222685357066 + 0.876461612491204 + 0.698520416286209 + 0.766959337903515 + 0.698520416286209 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(80.4, ` + `9.45, 68.8, 11.8, 51.2, 69.1, 15.5, 12.3, 129.9, 39.6, 90.2, ` + `71.3, 3.4, 56.6, 31.47, 23.7, 39.6, 90.2, 31.47, 68.8, 90.2, ` + `2.9, 4.1, 53.73, 59, 3.4, 89.8, 56.6, 39.6, 4.6, 5.1, 89.8, 71.3, ` + `8.2, 71.3, 8.3, 8.2, 68.8, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, ` + `1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, ` + `1, 1, 1, 1, 0), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(0.428895573591471, ` + `-0.498833871076256, -0.149830661743812, 0.547713434723109, -0.471191138855285, ` + `0.0885884003287424, 0.2961387837248, 0.160966885135322, 0.486528445998275, ` and 109 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[10]]$btree$y + 0.790837180224368 + 0.298972159062103 + 0.756447959466323 + 0.334686444776388 + 0.704271210729979 + 0.757337335865238 + 0.410185020274964 + 0.371723481813425 + 0.937584286045337 + 0.669881989971933 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(102.4, ` + `69.6, 8.2, 90.2, 23.7, 89.8, 56.6, 89.8, 91.33, 5.1, 129.9, 12.3, ` + `129.9, 8.2, 51.2, 11.8, 23.7, 83.8, 2.9, 77.4, 8.2, 9.45, 39.6, ` + `91.33, 53.73, 102.4, 9.45, 27.1, 90.2, 4.6, 39.6, 51.2, 27.1, ` + `2.9, 12.3, 88.1, 22.3, 27.1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, ` + `1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, ` + `1, 1, 1, 0, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(0.0255708109007852, ` + `0.12559682847857, -0.148203548115746, 0.0706053420547389, -0.0322368514978663, ` + `-0.110029799156235, -0.155361716202472, -0.110029799156235, 0.678449318884034, ` and 121 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[11]]$btree$y + 0.756490978896143 + 0.695529535150731 + 0.197216079414222 + 0.733816295551813 + 0.486105076957279 + 0.733072863311015 + 0.671367987324806 + 0.733072863311015 + 0.735916491632066 + 0.108980785296575 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(22.3, ` + `3.2, 88.1, 68.8, 12.3, 39.6, 3.2, 12.3, 80.4, 27.1, 8.2, 59, ` + `31.47, 59, 12.3, 71.3, 80.4, 59, 71.3, 83.8, 2.9, 31.47, 23.7, ` + `12.3, 4.6, 72.03, 71.3, 102.4, 102.4, 69.1, 51.2, 56.6, 56.6, ` + `71.3, 56.6, 77.4, 77.4, 9.45, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, ` + `0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, ` + `0, 0, 0, 0, 0, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(-0.617520610210315, ` + `-0.0364146689892928, 0.375292445076398, -0.149830661743812, 0.160966885135322, ` + `0.259723583341476, -0.0364146689892928, 0.160966885135322, 0.428895573591471, ` and 121 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[12]]$btree$y + 0.329370352822365 + 0.0803698435277383 + 0.596252485803642 + 0.569230378079239 + 0.29365606710808 + 0.528347189190506 + 0.0803698435277383 + 0.29365606710808 + 0.585471644898051 + 0.404868928320941 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(68.8, ` + `3.4, 3.4, 8.3, 2.4, 8.2, 12.3, 59, 23.7, 69.6, 4.1, 69.6, 8.3, ` + `5.1, 2.4, 4.1, 2.4, 83.8, 80.4, 68.8, 3.4, 11.8, 8.3, 88.1, 11.8, ` + `31.47, 22.3, 8.2, 56.6, 1.3, 68.8, 22.3, 90.2, 11.8, 90.2, 4.1, ` + `56.6, 71.3, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, ` + `0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1` + `), dim = c(38L, 2L), dimnames = list(NULL, c("time", "status"` + `)), type = "right", class = "Surv"), MGEc.1 = c(-0.149830661743812, ` + `-0.257859001561977, -0.257859001561977, -0.671223095093067, -0.576390672497128, ` + `-0.148203548115746, 0.160966885135322, -0.474079791435018, -0.0322368514978663, ` and 117 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[13]]$btree$y + 1.01569468195235 + 0.195632164672412 + 0.195632164672412 + 0.522194718009159 + 0.107396870554765 + 0.402194718009159 + 0.711189933320164 + 0.998833596649024 + 0.884801044431275 + 1.01707109707915 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(69.1, ` + `51.2, 31.47, 129.9, 4.6, 68.8, 59, 39.6, 89.8, 4.1, 77.4, 88.1, ` + `15.5, 91.33, 12.3, 12.3, 80.4, 4.6, 11.8, 102.4, 77.4, 77.4, ` + `9.45, 68.8, 89.8, 69.1, 129.9, 72.03, 77.4, 11.8, 11.8, 72.03, ` + `72.03, 59, 27.1, 51.2, 39.6, 12.3, 0, 0, 1, 0, 1, 0, 0, 1, 0, ` + `1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, ` + `1, 0, 0, 0, 1, 0, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, ` + ` c("time", "status")), type = "right", class = "Surv"), MGEc.1 = c(0.0885884003287424, ` + `-0.471191138855285, -0.16793386592959, 0.486528445998275, -0.169338484929163, ` + `-0.149830661743812, -0.474079791435018, 0.259723583341476, -0.110029799156235, ` and 109 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[14]]$btree$y + 0.472602709842458 + 0.440522994437922 + 0.405163620978062 + 0.581566212445575 + 0.0803698435277383 + 0.47206506098093 + 0.454501864837664 + 0.419733905125486 + 0.509700481287927 + 0.0263157894736842 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(31.47, ` + `53.73, 39.6, 83.8, 9.45, 71.3, 72.03, 27.1, 12.3, 39.6, 89.8, ` + `9.45, 9.45, 27.1, 12.3, 90.2, 1.3, 83.8, 68.8, 90.2, 27.1, 83.8, ` + `39.6, 80.4, 11.8, 3.4, 56.6, 15.5, 69.1, 77.4, 80.4, 56.6, 22.3, ` + `3.2, 88.1, 15.5, 69.6, 56.6, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, ` + `1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, ` + `1, 1, 0, 1, 0, 0), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(-0.16793386592959, ` + `0.0480491126589312, 0.259723583341476, -0.0360203071525677, -0.498833871076256, ` + `-0.434279176291204, 0.667863127862309, 0.000227688335335926, ` and 110 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[15]]$btree$y + 0.52746643955763 + 0.60614963307257 + 0.556203832337345 + 0.712439104276265 + 0.166834879992775 + 0.66825492103562 + 0.670835277336874 + 0.483988178688065 + 0.262601009025033 + 0.556203832337345 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(2.9, ` + `56.6, 56.6, 22.3, 11.8, 27.1, 3.4, 90.2, 3.2, 12.3, 8.2, 89.8, ` + `4.6, 27.1, 68.8, 4.1, 68.8, 69.1, 5.1, 89.8, 2.9, 91.33, 39.6, ` + `27.1, 11.8, 51.2, 8.3, 89.8, 1.3, 89.8, 129.9, 83.8, 15.5, 4.6, ` + `2.4, 4.6, 4.6, 5.1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, ` + `0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, ` + `1, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", "status"` + `)), type = "right", class = "Surv"), MGEc.1 = c(0.330829257500481, ` + `-0.155361716202472, -0.155361716202472, -0.617520610210315, 0.547713434723109, ` + `0.000227688335335926, -0.257859001561977, 0.0706053420547389, ` and 125 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[16]]$btree$y + 0.108898372056267 + 0.927395867243134 + 0.927395867243134 + 0.721843314175983 + 0.571592687609567 + 0.888509980842649 + 0.168613167065179 + 0.971686232770467 + 0.138310136762149 + 0.619211735228614 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(69.1, ` + `71.3, 129.9, 71.3, 11.8, 4.1, 4.6, 8.3, 89.8, 31.47, 15.5, 2.4, ` + `22.3, 1.3, 83.8, 11.8, 11.8, 71.3, 59, 69.1, 15.5, 4.1, 3.4, ` + `1.3, 59, 71.3, 12.3, 69.1, 11.8, 69.6, 22.3, 69.6, 51.2, 56.6, ` + `5.1, 71.3, 77.4, 71.3, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, ` + `1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, ` + `1, 0, 0, 0), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(0.0885884003287424, ` + `-0.434279176291204, 0.486528445998275, -0.244315812326253, 0.547713434723109, ` + `-0.173108046693142, -0.169338484929163, -0.671223095093067, -0.110029799156235, ` and 126 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[17]]$btree$y + 0.714873623630795 + 0.71768307872421 + 0.792516746212429 + 0.71768307872421 + 0.40257674704056 + 0.167804314708339 + 0.199054314708339 + 0.264645712557802 + 0.74130804200974 + 0.666819171282985 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(56.6, ` + `5.1, 9.45, 53.73, 71.3, 80.4, 77.4, 15.5, 12.3, 91.33, 12.3, ` + `56.6, 102.4, 59, 8.2, 39.6, 59, 77.4, 8.3, 91.33, 3.4, 11.8, ` + `3.4, 2.4, 11.8, 11.8, 3.2, 2.4, 22.3, 129.9, 2.4, 2.9, 71.3, ` + `90.2, 22.3, 91.33, 56.6, 3.2, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, ` + `0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, ` + `1, 0, 1, 0, 0, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(-0.155361716202472, ` + `-0.239593761387222, -0.498833871076256, 0.0480491126589312, -0.244315812326253, ` + `0.428895573591471, -0.0263613327352018, 0.2961387837248, 0.160966885135322, ` and 123 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[18]]$btree$y + 0.82783388762001 + 0.262175659737579 + 0.369409741109592 + 0.818901703747737 + 0.873584097697505 + 0.901905656316907 + 0.892568878750071 + 0.619369925852385 + 0.571750878233337 + 0.935922649252079 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(39.6, ` + `68.8, 12.3, 3.2, 51.2, 102.4, 72.03, 69.6, 11.8, 2.4, 72.03, ` + `1.3, 129.9, 22.3, 15.5, 129.9, 56.6, 51.2, 71.3, 11.8, 4.6, 4.6, ` + `23.7, 8.2, 129.9, 4.1, 23.7, 4.1, 2.9, 12.3, 5.1, 3.4, 5.1, 2.4, ` + `91.33, 1.3, 15.5, 5.1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, ` + `1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, ` + `0, 1, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(0.259723583341476, ` + `-0.149830661743812, 0.160966885135322, -0.0364146689892928, -0.471191138855285, ` + `0.0255708109007852, 0.667863127862309, 0.12559682847857, 0.547713434723109, ` and 126 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[19]]$btree$y + 0.976469023224658 + 1.01278788291197 + 0.667605970540477 + 0.167901929511837 + 0.990897063374411 + 1.05457944748367 + 1.01680534581573 + 1.01378292016367 + 0.572367875302382 + 0.108187134502924 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(69.1, ` + `129.9, 51.2, 1.3, 22.3, 8.3, 102.4, 77.4, 90.2, 12.3, 91.33, ` + `1.3, 8.3, 1.3, 8.2, 11.8, 15.5, 3.4, 4.1, 2.4, 3.2, 8.2, 68.8, ` + `69.6, 59, 51.2, 31.47, 71.3, 8.2, 8.3, 1.3, 1.3, 31.47, 68.8, ` + `91.33, 2.9, 4.1, 22.3, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, ` + `1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, ` + `0, 1, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(0.0885884003287424, ` + `0.486528445998275, -0.471191138855285, -0.0818898414911842, -0.617520610210315, ` + `-0.671223095093067, 0.0255708109007852, -0.0263613327352018, ` and 109 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[20]]$btree$y + 1.00796689685524 + 1.10136454348033 + 0.980469892338974 + 0.131578947368421 + 0.825161741550931 + 0.563800003873404 + 1.05912054212852 + 1.02071690453597 + 1.04037956698336 + 0.661419051492452 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(27.1, ` + `69.6, 83.8, 56.6, 1.3, 91.33, 59, 72.03, 11.8, 39.6, 88.1, 4.6, ` + `23.7, 53.73, 129.9, 22.3, 5.1, 71.3, 27.1, 1.3, 102.4, 90.2, ` + `15.5, 80.4, 8.2, 71.3, 56.6, 89.8, 4.1, 102.4, 77.4, 56.6, 89.8, ` + `89.8, 69.6, 1.3, 71.3, 59, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, ` + `1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, ` + `0, 0, 1, 0, 0), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(0.000227688335335926, ` + `0.12559682847857, -0.0360203071525677, -0.155361716202472, -0.0818898414911842, ` + `0.678449318884034, -0.474079791435018, 0.667863127862309, 0.547713434723109, ` and 130 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[21]]$btree$y + 0.408346108259906 + 0.480549116020831 + 0.495791873027668 + 0.46659447932443 + 0.0789473684210526 + 0.50387482797566 + 0.469170719945304 + 0.483157559649465 + 0.230741656517523 + 0.448346108259906 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(22.3, ` + `3.4, 2.4, 77.4, 91.33, 3.2, 90.2, 4.1, 56.6, 2.4, 102.4, 4.6, ` + `39.6, 27.1, 53.73, 69.1, 56.6, 8.3, 51.2, 4.1, 129.9, 69.6, 90.2, ` + `51.2, 88.1, 102.4, 53.73, 1.3, 4.6, 2.4, 77.4, 88.1, 12.3, 4.6, ` + `91.33, 88.1, 53.73, 71.3, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, ` + `1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, ` + `1, 0, 0, 0, 0), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(-0.617520610210315, ` + `-0.257859001561977, -0.576390672497128, -0.0263613327352018, ` + `0.678449318884034, -0.0364146689892928, 0.0706053420547389, -0.173108046693142, ` and 109 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[22]]$btree$y + 0.445110241062253 + 0.167111665563678 + 0.107396870554765 + 0.531689627046747 + 0.544127682317071 + 0.136808635260648 + 0.543118708702178 + 0.229611665563678 + 0.513117369356671 + 0.107396870554765 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(102.4, ` + `68.8, 88.1, 91.33, 8.2, 4.6, 3.2, 31.47, 91.33, 129.9, 88.1, ` + `15.5, 8.3, 4.1, 90.2, 31.47, 1.3, 71.3, 39.6, 8.2, 53.73, 53.73, ` + `69.1, 11.8, 51.2, 31.47, 8.2, 27.1, 4.6, 51.2, 9.45, 22.3, 89.8, ` + `69.6, 90.2, 53.73, 3.4, 31.47, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, ` + `0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, ` + `1, 0, 0, 0, 0, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, ` + ` c("time", "status")), type = "right", class = "Surv"), MGEc.1 = c(0.0255708109007852, ` + `-0.149830661743812, 0.375292445076398, 0.678449318884034, -0.148203548115746, ` + `-0.169338484929163, -0.0364146689892928, -0.16793386592959, 0.678449318884034, ` and 121 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[23]]$btree$y + 0.747440009301266 + 0.707696449887011 + 0.730525339669604 + 0.734345925887105 + 0.262265552261682 + 0.168515552261682 + 0.0533428165007112 + 0.663540882240161 + 0.734345925887105 + 0.779968220131386 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(4.6, ` + `2.9, 56.6, 11.8, 89.8, 22.3, 22.3, 51.2, 12.3, 53.73, 39.6, 5.1, ` + `39.6, 59, 4.6, 27.1, 3.2, 11.8, 3.2, 9.45, 2.9, 71.3, 12.3, 51.2, ` + `23.7, 69.6, 69.1, 69.1, 22.3, 69.6, 71.3, 102.4, 22.3, 69.6, ` + `59, 39.6, 3.4, 8.2, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, ` + `1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, ` + `1, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", "status"` + `)), type = "right", class = "Surv"), MGEc.1 = c(-0.169338484929163, ` + `0.330829257500481, -0.155361716202472, 0.547713434723109, -0.110029799156235, ` + `-0.617520610210315, -0.617520610210315, -0.471191138855285, 0.160966885135322, ` and 124 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[24]]$btree$y + 0.198204959814867 + 0.0526315789473684 + 0.923214031635072 + 0.36970768771359 + 1.00253928908376 + 0.613297431303334 + 0.613297431303334 + 0.910311730724742 + 0.446630764636667 + 0.91635669763273 and 66 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(8.2, ` + `8.3, 8.3, 129.9, 22.3, 89.8, 69.1, 77.4, 8.3, 1.3, 69.1, 71.3, ` + `9.45, 83.8, 1.3, 102.4, 27.1, 11.8, 59, 59, 90.2, 102.4, 91.33, ` + `4.6, 72.03, 39.6, 56.6, 71.3, 72.03, 22.3, 68.8, 12.3, 91.33, ` + `12.3, 5.1, 72.03, 4.6, 23.7, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, ` + `0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, ` + `0, 1, 1, 0, 1, 1), dim = c(38L, 2L), dimnames = list(NULL, c("time", ` + `"status")), type = "right", class = "Surv"), MGEc.1 = c(-0.148203548115746, ` + `-0.671223095093067, -0.671223095093067, 0.486528445998275, -0.617520610210315, ` + `-0.110029799156235, 0.0885884003287424, -0.0263613327352018, ` and 108 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$mtrees[[25]]$btree$y + 0.167901929511837 + 0.261651929511837 + 0.261651929511837 + 0.734391615007751 + 0.485923047920886 + 0.684692166535297 + 0.659036840266473 + 0.669323758528852 + 0.261651929511837 + 0.0526315789473684 and 66 more ... parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$object$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$object$call - `dummy_call()` + `bagging.data.frame(formula = Surv(time, cens) ~ MGEc.1 + MGEc.2 + ` + ` MGEc.3 + MGEc.4 + MGEc.5 + MGEc.6 + MGEc.7 + MGEc.8 + MGEc.9 + ` + ` MGEc.10 + IPI, data = DLBCL, coob = TRUE)` class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree) - "butchered_rpart" "rpart" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree, 'butcher_disabled')` is a character vector ('summary()', 'printcp()', 'xpred.rpart()') `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree, 'butcher_disabled')` is absent parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$call vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$call - `dummy_call()` + `rpart(formula = y ~ ., data = structure(list(y = structure(c(39.6, ` + `88.1, 68.8, 69.6, 51.2, 2.9, 4.6, 68.8, 5.1, 3.4, 89.8, 27.1, ` + `12.3, 8.2, 69.1, 88.1, 129.9, 5.1, 102.4, 31.47, 31.47, 8.3, ` + `59, 15.5, 1.3, 77.4, 72.03, 4.6, 23.7, 1.3, 102.4, 27.1, 27.1, ` + `31.47, 91.33, 5.1, 56.6, 39.6, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, ` + `0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, ` + `1, 1, 1, 0, 1, 0, 1), dim = c(38L, 2L), dimnames = list(NULL, ` + ` c("time", "status")), type = "right", class = "Surv"), MGEc.1 = c(0.259723583341476, ` + `0.375292445076398, -0.149830661743812, 0.12559682847857, -0.471191138855285, ` + `0.330829257500481, -0.169338484929163, -0.149830661743812, -0.239593761387222, ` and 125 more ... class(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$terms) vs class(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$terms) - "butchered_terms" "terms" "formula" `attr(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$terms, '.Environment')` is `attr(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$terms, '.Environment')` is `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control` is length 2 `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control` is length 9 names(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control) vs names(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control) - "" - "usesurrogate" + "minsplit" + "minbucket" + "cp" + "maxcompete" + "maxsurrogate" + "usesurrogate" + "surrogatestyle" + "maxdepth" + "xval" `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control[[1]]` is NULL `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control[[1]]` is an integer vector (20) parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control[[2]] vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control[[2]] - 2.0 + 7.0 `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control[[3]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control[[3]]` is a double vector (0.01) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control[[4]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control[[4]]` is an integer vector (4) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control[[5]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control[[5]]` is an integer vector (5) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control[[6]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control[[6]]` is an integer vector (2) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control[[7]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control[[7]]` is an integer vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control[[8]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control[[8]]` is an integer vector (30) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$control[[9]]` is absent `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$control[[9]]` is a double vector (0) `parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$functions` is a call `parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$functions` is a list `dim(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$y)` is absent `dim(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$y)` is an integer vector (38, 2) `dimnames(parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$y)` is absent `dimnames(parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$y)` is a list parent.env(attr(actual[[1]]$call[[2]], '.Environment'))$tree$y vs parent.env(attr(expected[[1]]$call[[2]], '.Environment'))$tree$y + 0.735500906572494 + 0.857161511112817 + 0.808748115903987 + 0.810754888762385 + 0.764599113019251 + 0.0804093567251462 + 0.167804314708339 + 0.808748115903987 + 0.261554314708339 + 0.108980785296575 and 66 more ... [ FAIL 1 | WARN 0 | SKIP 35 | PASS 219 ] Error: Test failures Execution halted Package: cardx Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [153s/88s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > test_check("cardx") Loading required package: cardx Attaching package: 'cardx' The following object is masked from 'package:testthat': matches Starting 2 test processes [ FAIL 1 | WARN 0 | SKIP 45 | PASS 520 ] ══ Skipped tests (45) ══════════════════════════════════════════════════════════ • On CRAN (45): 'test-ard_attributes.survey.design.R:8:3', 'test-ard_aod_wald_test.R:12:3', 'test-ard_car_vif.R:4:3', 'test-ard_car_vif.R:29:3', 'test-ard_car_anova.R:14:3', 'test-ard_car_anova.R:18:3', 'test-ard_categorical_ci.survey.design.R:7:3', 'test-ard_categorical.survey.design.R:428:3', 'test-ard_categorical.survey.design.R:1195:3', 'test-ard_continuous_ci.survey.design.R:7:3', 'test-ard_continuous_ci.survey.design.R:194:3', 'test-ard_dichotomous.survey.design.R:413:3', 'test-ard_effectsize_cohens_d.R:38:3', 'test-ard_effectsize_hedges_g.R:34:3', 'test-ard_proportion_ci.R:92:3', 'test-ard_proportion_ci.R:113:3', 'test-ard_regression.R:6:3', 'test-ard_regression.R:30:3', 'test-ard_regression_basic.R:12:3', 'test-ard_stats_aov.R:25:3', 'test-ard_stats_kruskal_test.R:22:3', 'test-ard_stats_mood_test.R:22:3', 'test-ard_stats_oneway_test.R:24:3', 'test-ard_stats_poisson_test.R:74:3', 'test-ard_survey_svychisq.R:28:3', 'test-ard_survey_svyranktest.R:18:3', 'test-ard_survival_survfit.R:4:3', 'test-ard_survival_survfit.R:15:3', 'test-ard_survival_survfit.R:26:3', 'test-ard_survival_survfit.R:37:3', 'test-ard_survival_survfit.R:57:3', 'test-ard_survival_survfit.R:91:3', 'test-ard_survival_survfit.R:116:3', 'test-ard_survival_survfit.R:162:3', 'test-ard_survival_survfit.R:180:3', 'test-ard_survival_survfit.R:189:3', 'test-ard_survival_survfit_diff.R:40:3', 'test-ard_total_n.survey.design.R:4:3', 'test-construction_helpers.R:4:3', 'test-construction_helpers.R:104:3', 'test-proportion_ci.R:12:3', 'test-proportion_ci.R:138:3', 'test-ard_continuous.survey.design.R:70:3', 'test-ard_continuous.survey.design.R:322:3', 'test-ard_continuous.survey.design.R:338:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-ard_survival_survfit.R:111:3'): ard_survival_survfit() errors are properly handled ── `ard_survival_survfit(x, times = 25)` did not throw the expected error. [ FAIL 1 | WARN 0 | SKIP 45 | PASS 520 ] Error: Test failures Execution halted Package: CauchyCP Check: examples New result: ERROR Running examples in ‘CauchyCP-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: CauchyCP > ### Title: A robust test under non-proportional hazards using Cauchy > ### combination of change-point Cox regressions. > ### Aliases: CauchyCP > > ### ** Examples > > data(gast) > CauchyCP(time=gast$time, status=gast$status, x=gast$trt) Error in anova.coxphlist(object, test = test) : models do not have the same strata Calls: CauchyCP -> anova -> anova.coxph -> anova.coxphlist Execution halted Package: Epi Check: examples New result: ERROR Running examples in ‘Epi-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: effx.match > ### Title: Function to calculate effects for individually matched > ### case-control studies > ### Aliases: effx.match > ### Keywords: models regression > > ### ** Examples > > library(Epi) > library(survival) > data(bdendo) > > # d is the case-control variable, set is the matching variable. > # The variable est is a factor and refers to estrogen use (no,yes) > # The variable hyp is a factor with 2 levels and refers to hypertension (no, yes) > # effect of est on the odds of being a case > effx.match(d,exposure=est,match=set,data=bdendo) --------------------------------------------------------------------------- response : d exposure : est est is a factor with levels: No / Yes baseline is No effects are measured as odds ratios --------------------------------------------------------------------------- effect of est on d number of observations 315 Effect 2.5% 97.5% 7.95 3.49 18.10 Test for no effects of exposure: chisq= 35.35 df= 1 p-value= <2e-16 > # effect of est on the odds of being a case, stratified by hyp > effx.match(d,exposure=est,match=set,strata=hyp,data=bdendo) --------------------------------------------------------------------------- response : d exposure : est stratified by : hyp est is a factor with levels: No / Yes baseline is No hyp is a factor with levels: No/Yes effects are measured as odds ratios --------------------------------------------------------------------------- effect of est on d stratified by hyp Error in model.frame.default(formula = Surv(rep(1, 315L), response) ~ : invalid type (closure) for variable 'strata' Calls: effx.match ... coxph -> eval -> eval -> -> model.frame.default Execution halted Package: epiDisplay Check: examples New result: ERROR Running examples in ‘epiDisplay-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: Matched case-control study > ### Title: Datasets on a matched case-control study of esophageal cancer > ### Aliases: VC1to1 VC1to6 > ### Keywords: datasets > > ### ** Examples > > data(VC1to6) > .data <- VC1to6 > des(.data) No. of observations = 119 Variable Class Description 1 matset integer 2 case integer 3 smoking integer 4 rubber integer 5 alcohol integer > with(.data, matchTab(case, alcohol, matset)) Error in strata(strata) : all arguments must be vectors Calls: with ... -> model.frame.default -> eval -> eval -> strata Execution halted Package: fic Check: examples New result: ERROR Running examples in ‘fic-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: fic.coxph > ### Title: Focused information criteria for Cox proportional hazard > ### regression models > ### Aliases: fic.coxph > > ### ** Examples > > > ## Example of focused covariate selection in a Cox model > ## For more information see the main package vignette. > > library(survival) > wide <- coxph(Surv(years, death==1) ~ sex + thick_centred + infilt + + epith + ulcer + depth + age, data=melanoma) > > ## Define models to be selected from > ## Sex included in all models > ## Select between models including all combinations of other covariates > inds0 <- expand_inds(c(1,0,0,0,0,0,0), wide) > combs <- all_inds(wide, inds0) > > ## Covariate values defining focus > newdata <- with(melanoma, + data.frame(sex = c("female","male"), + thick_centred = tapply(thick_centred, sex, mean), + infilt=4, epith=1, ulcer=1, depth=2, + age = tapply(age, sex, mean))) > X <- newdata_to_X(newdata, wide, intercept=FALSE) > > ## Focus is 5-year survival for these covariate values > ficall <- fic(wide, inds=combs, inds0=inds0, focus="survival", X=X, t=5) Error in eval(predvars, data, env) : object 'XZi' not found Calls: fic ... eval -> -> model.frame.default -> eval -> eval Execution halted Package: fic Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘fic.Rnw’ using knitr Quitting from lines 784-792 [unnamed-chunk-16] (fic.Rnw) Error: processing vignette 'fic.Rnw' failed with diagnostics: object 'XZi' not found --- failed re-building ‘fic.Rnw’ --- re-building ‘linear.Rnw’ using knitr --- finished re-building ‘linear.Rnw’ --- re-building ‘loss.Rnw’ using knitr --- finished re-building ‘loss.Rnw’ --- re-building ‘multistate.Rnw’ using knitr --- finished re-building ‘multistate.Rnw’ --- re-building ‘skewnormal.Rnw’ using knitr --- finished re-building ‘skewnormal.Rnw’ --- re-building ‘survival.Rnw’ using knitr --- finished re-building ‘survival.Rnw’ SUMMARY: processing the following file failed: ‘fic.Rnw’ Error: Vignette re-building failed. Execution halted Package: fic Check: tests New result: ERROR Running ‘test_base.R’ [6s/7s] Running the tests in ‘tests/test_base.R’ failed. Complete output: > if (require("testthat")){ + test_check("fic") + } Loading required package: testthat Loading required package: fic Error in X %*% par : non-conformable arguments Error in object$coefficients : $ operator is invalid for atomic vectors Error in X %*% par : non-conformable arguments Error in par %*% X : non-conformable arguments [ FAIL 1 | WARN 0 | SKIP 0 | PASS 45 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_cox.R:21:1'): (code run outside of `test_that()`) ────────────── Error in `eval(predvars, data, env)`: object 'XZi' not found Backtrace: ▆ 1. ├─fic::fic(...) at test_cox.R:21:1 2. └─fic:::fic.coxph(...) 3. ├─fic::fit_submodels(wide, inds) 4. └─fic:::fit_submodels.coxph(wide, inds) 5. ├─base::eval(call) 6. │ └─base::eval(call) 7. ├─survival::coxph(...) 8. │ └─base::eval(tform, parent.frame()) 9. │ └─base::eval(tform, parent.frame()) 10. ├─stats::model.frame(...) 11. └─stats::model.frame.default(...) 12. └─base::eval(predvars, data, env) 13. └─base::eval(predvars, data, env) [ FAIL 1 | WARN 0 | SKIP 0 | PASS 45 ] Error: Test failures Execution halted Package: insight Check: tests New result: ERROR Running ‘testthat.R’ [289s/145s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(insight) > test_check("insight") Starting 2 test processes [ FAIL 2 | WARN 0 | SKIP 73 | PASS 3393 ] ══ Skipped tests (73) ══════════════════════════════════════════════════════════ • On CRAN (65): 'test-GLMMadaptive.R:1:1', 'test-averaging.R:3:1', 'test-brms.R:1:1', 'test-brms_aterms.R:1:1', 'test-brms_gr_random_effects.R:1:1', 'test-brms_missing.R:1:1', 'test-blmer.R:249:3', 'test-clean_names.R:103:3', 'test-clean_parameters.R:2:3', 'test-clean_parameters.R:35:3', 'test-clmm.R:165:3', 'test-cpglmm.R:145:3', 'test-export_table.R:4:3', 'test-export_table.R:8:3', 'test-export_table.R:106:3', 'test-export_table.R:133:3', 'test-export_table.R:164:3', 'test-export_table.R:193:3', 'test-export_table.R:205:3', 'test-export_table.R:233:3', 'test-find_smooth.R:31:3', 'test-find_random.R:27:3', 'test-format_table.R:1:1', 'test-format_table_ci.R:71:3', 'test-gam.R:1:1', 'test-get_data.R:385:1', 'test-get_loglikelihood.R:93:3', 'test-get_loglikelihood.R:158:3', 'test-get_predicted.R:2:1', 'test-get_priors.R:3:3', 'test-get_varcov.R:40:3', 'test-get_datagrid.R:215:3', 'test-get_datagrid.R:261:3', 'test-is_converged.R:28:1', 'test-lme.R:34:3', 'test-lme.R:210:3', 'test-glmmTMB.R:71:3', 'test-glmmTMB.R:755:3', 'test-glmmTMB.R:787:3', 'test-marginaleffects.R:1:1', 'test-mgcv.R:1:1', 'test-mipo.R:1:1', 'test-mvrstanarm.R:1:1', 'test-panelr-asym.R:142:3', 'test-panelr.R:272:3', 'test-phylolm.R:5:1', 'test-r2_nakagawa_bernoulli.R:1:1', 'test-r2_nakagawa_beta.R:1:1', 'test-r2_nakagawa_binomial.R:1:1', 'test-r2_nakagawa_gamma.R:1:1', 'test-r2_nakagawa_linear.R:1:1', 'test-r2_nakagawa_negbin.R:1:1', 'test-r2_nakagawa_negbin_zi.R:1:1', 'test-r2_nakagawa_ordered_beta.R:1:1', 'test-r2_nakagawa_poisson.R:1:1', 'test-r2_nakagawa_poisson_zi.R:1:1', 'test-r2_nakagawa_truncated_poisson.R:1:1', 'test-r2_nakagawa_tweedie.R:1:1', 'test-rlmer.R:259:3', 'test-rqss.R:1:1', 'test-rstanarm.R:1:1', 'test-spatial.R:1:1', 'test-svylme.R:1:1', 'test-vgam.R:1:1', 'test-weightit.R:1:1' • On Linux (3): 'test-BayesFactorBF.R:1:1', 'test-MCMCglmm.R:1:1', 'test-get_data.R:150:3' • Package `logistf` is loaded and breaks `mmrm::mmrm()` (1): 'test-mmrm.R:4:1' • TRUE is TRUE (1): 'test-fixest.R:2:1' • works interactively (2): 'test-coxph-panel.R:34:3', 'test-coxph.R:38:3' • {bigglm} is not installed (1): 'test-model_info.R:24:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-survfit.R:16:3'): find_response ────────────────────────────── find_response(m1) (`actual`) not identical to "survival::Surv(time, status)" (`expected`). `actual`: "Surv(time, status)" `expected`: "survival::Surv(time, status)" ── Failure ('test-survfit.R:31:3'): find_formula ─────────────────────────────── find_formula(m1) (`actual`) not equal to list(conditional = as.formula("survival::Surv(time, status) ~ sex + age + ph.ecog")) (`expected`). `actual[[1]]`: `Surv(time, status) ~ sex + age + ph.ecog` `expected[[1]]`: `survival::Surv(time, status) ~ sex + age + ph.ecog` [ FAIL 2 | WARN 0 | SKIP 73 | PASS 3393 ] Error: Test failures Execution halted Package: ipred Check: examples New result: ERROR Running examples in ‘ipred-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: sbrier > ### Title: Model Fit for Survival Data > ### Aliases: sbrier > ### Keywords: survival > > ### ** Examples > > > library("survival") > data("DLBCL", package = "ipred") > smod <- Surv(DLBCL$time, DLBCL$cens) > > KM <- survfit(smod ~ 1) > # integrated Brier score up to max(DLBCL$time) > sbrier(smod, KM) [,1] [1,] 0.2237226 attr(,"names") [1] "integrated Brier score" attr(,"time") [1] 1.3 129.9 > > # integrated Brier score up to time=50 > sbrier(smod, KM, btime=c(0, 50)) Warning in sbrier(smod, KM, btime = c(0, 50)) : btime[1] is smaller than min(time) [,1] [1,] 0.2174081 attr(,"names") [1] "integrated Brier score" attr(,"time") [1] 1.3 39.6 > > # Brier score for time=50 > sbrier(smod, KM, btime=50) Brier score 0.249375 attr(,"time") [1] 50 > > # a "real" model: one single survival tree with Intern. Prognostic Index > # and mean gene expression in the first cluster as predictors > mod <- bagging(Surv(time, cens) ~ MGEc.1 + IPI, data=DLBCL, nbagg=1) > > # this is a list of survfit objects (==KM-curves), one for each observation > # in DLBCL > pred <- predict(mod, newdata=DLBCL) > > # integrated Brier score up to max(time) > sbrier(smod, pred) [,1] [1,] 0.1442559 attr(,"names") [1] "integrated Brier score" attr(,"time") [1] 1.3 129.9 > > # Brier score at time=50 > sbrier(smod, pred, btime=50) Brier score 0.1774478 attr(,"time") [1] 50 > # artificial examples and illustrations > > cleans <- function(x) { attr(x, "time") <- NULL; names(x) <- NULL; x } > > n <- 100 > time <- rpois(n, 20) > cens <- rep(1, n) > > # checks, Graf et al. page 2536, no censoring at all! > # no information: \pi(t) = 0.5 > > a <- sbrier(Surv(time, cens), rep(0.5, n), time[50]) > stopifnot(all.equal(cleans(a),0.25)) > > # some information: \pi(t) = S(t) > > n <- 100 > time <- 1:100 > mod <- survfit(Surv(time, cens) ~ 1) > a <- sbrier(Surv(time, cens), rep(list(mod), n)) > mymin <- mod$surv * (1 - mod$surv) > cleans(a) [,1] [1,] 0.1682833 > sum(mymin)/diff(range(time)) [1] 0.1683333 > > # independent of ordering > rand <- sample(1:100) > b <- sbrier(Surv(time, cens)[rand], rep(list(mod), n)[rand]) > stopifnot(all.equal(cleans(a), cleans(b))) > > ## Don't show: > # total information: \pi(t | X) known for every obs > > time <- 1:10 > cens <- rep(1,10) > pred <- diag(10) > pred[upper.tri(pred)] <- 1 > diag(pred) <- 0 > # > # a <- sbrier(Surv(time, cens), pred) > # stopifnot(all.equal(a, 0)) > # > ## End(Don't show) > > # 2 groups at different risk > > time <- c(1:10, 21:30) > strata <- c(rep(1, 10), rep(2, 10)) > cens <- rep(1, length(time)) > > # no information about the groups > > a <- sbrier(Surv(time, cens), survfit(Surv(time, cens) ~ 1)) > b <- sbrier(Surv(time, cens), rep(list(survfit(Surv(time, cens) ~1)), 20)) > stopifnot(all.equal(a, b)) > > # risk groups known > > mod <- survfit(Surv(time, cens) ~ strata) Error in model.frame.default(formula = Surv(time, cens) ~ strata) : invalid type (closure) for variable 'strata' Calls: survfit ... eval.parent -> eval -> eval -> -> model.frame.default Execution halted Package: lnmixsurv Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘compare.Rmd’ using rmarkdown ** Processing: /home/hornik/tmp/CRAN_recheck/lnmixsurv.Rcheck/vign_test/lnmixsurv/vignettes/compare_files/figure-html/unnamed-chunk-5-1.png 672x288 pixels, 3x8 bits/pixel, RGB Input IDAT size = 58490 bytes Input file size = 58652 bytes Trying: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 43503 zc = 9 zm = 8 zs = 1 f = 0 zc = 1 zm = 8 zs = 2 f = 0 zc = 9 zm = 8 zs = 3 f = 0 zc = 9 zm = 8 zs = 0 f = 5 zc = 9 zm = 8 zs = 1 f = 5 zc = 1 zm = 8 zs = 2 f = 5 zc = 9 zm = 8 zs = 3 f = 5 Selecting parameters: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 43503 Output IDAT size = 43503 bytes (14987 bytes decrease) Output file size = 43581 bytes (15071 bytes = 25.70% decrease) --- finished re-building ‘compare.Rmd’ --- re-building ‘expectation_maximization.Rmd’ using rmarkdown ** Processing: /home/hornik/tmp/CRAN_recheck/lnmixsurv.Rcheck/vign_test/lnmixsurv/vignettes/expectation_maximization_files/figure-html/unnamed-chunk-4-1.png 672x288 pixels, 3x8 bits/pixel, RGB Input IDAT size = 22844 bytes Input file size = 22946 bytes Trying: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 19548 zc = 9 zm = 8 zs = 1 f = 0 zc = 1 zm = 8 zs = 2 f = 0 zc = 9 zm = 8 zs = 3 f = 0 zc = 9 zm = 8 zs = 0 f = 5 zc = 9 zm = 8 zs = 1 f = 5 zc = 1 zm = 8 zs = 2 f = 5 zc = 9 zm = 8 zs = 3 f = 5 Selecting parameters: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 19548 Output IDAT size = 19548 bytes (3296 bytes decrease) Output file size = 19626 bytes (3320 bytes = 14.47% decrease) ** Processing: /home/hornik/tmp/CRAN_recheck/lnmixsurv.Rcheck/vign_test/lnmixsurv/vignettes/expectation_maximization_files/figure-html/unnamed-chunk-8-1.png 672x288 pixels, 3x8 bits/pixel, RGB Input IDAT size = 20995 bytes Input file size = 21097 bytes Trying: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 18484 zc = 9 zm = 8 zs = 1 f = 0 zc = 1 zm = 8 zs = 2 f = 0 zc = 9 zm = 8 zs = 3 f = 0 zc = 9 zm = 8 zs = 0 f = 5 zc = 9 zm = 8 zs = 1 f = 5 zc = 1 zm = 8 zs = 2 f = 5 zc = 9 zm = 8 zs = 3 f = 5 Selecting parameters: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 18484 Output IDAT size = 18484 bytes (2511 bytes decrease) Output file size = 18562 bytes (2535 bytes = 12.02% decrease) --- finished re-building ‘expectation_maximization.Rmd’ --- re-building ‘intercept_only.Rmd’ using rmarkdown ** Processing: /home/hornik/tmp/CRAN_recheck/lnmixsurv.Rcheck/vign_test/lnmixsurv/vignettes/intercept_only_files/figure-html/unnamed-chunk-3-1.png 672x288 pixels, 3x8 bits/pixel, RGB Input IDAT size = 58934 bytes Input file size = 59096 bytes Trying: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 42865 zc = 9 zm = 8 zs = 1 f = 0 zc = 1 zm = 8 zs = 2 f = 0 zc = 9 zm = 8 zs = 3 f = 0 zc = 9 zm = 8 zs = 0 f = 5 zc = 9 zm = 8 zs = 1 f = 5 zc = 1 zm = 8 zs = 2 f = 5 zc = 9 zm = 8 zs = 3 f = 5 Selecting parameters: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 42865 Output IDAT size = 42865 bytes (16069 bytes decrease) Output file size = 42943 bytes (16153 bytes = 27.33% decrease) Quitting from lines 55-71 [unnamed-chunk-4] (intercept_only.Rmd) Error: processing vignette 'intercept_only.Rmd' failed with diagnostics: Response must be a survival object --- failed re-building ‘intercept_only.Rmd’ --- re-building ‘lnmixsurv.Rmd’ using rmarkdown ** Processing: /home/hornik/tmp/CRAN_recheck/lnmixsurv.Rcheck/vign_test/lnmixsurv/vignettes/lnmixsurv_files/figure-html/unnamed-chunk-4-1.png 672x288 pixels, 3x8 bits/pixel, RGB Input IDAT size = 28315 bytes Input file size = 28429 bytes Trying: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 22698 zc = 9 zm = 8 zs = 1 f = 0 zc = 1 zm = 8 zs = 2 f = 0 zc = 9 zm = 8 zs = 3 f = 0 zc = 9 zm = 8 zs = 0 f = 5 zc = 9 zm = 8 zs = 1 f = 5 zc = 1 zm = 8 zs = 2 f = 5 zc = 9 zm = 8 zs = 3 f = 5 Selecting parameters: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 22698 Output IDAT size = 22698 bytes (5617 bytes decrease) Output file size = 22776 bytes (5653 bytes = 19.88% decrease) --- finished re-building ‘lnmixsurv.Rmd’ --- re-building ‘parallel_computation.Rmd’ using rmarkdown ** Processing: /home/hornik/tmp/CRAN_recheck/lnmixsurv.Rcheck/vign_test/lnmixsurv/vignettes/parallel_computation_files/figure-html/unnamed-chunk-4-1.png 288x288 pixels, 3x8 bits/pixel, RGB Input IDAT size = 12122 bytes Input file size = 12212 bytes Trying: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 9526 zc = 9 zm = 8 zs = 1 f = 0 zc = 1 zm = 8 zs = 2 f = 0 zc = 9 zm = 8 zs = 3 f = 0 zc = 9 zm = 8 zs = 0 f = 5 zc = 9 zm = 8 zs = 1 f = 5 zc = 1 zm = 8 zs = 2 f = 5 zc = 9 zm = 8 zs = 3 f = 5 Selecting parameters: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 9526 Output IDAT size = 9526 bytes (2596 bytes decrease) Output file size = 9604 bytes (2608 bytes = 21.36% decrease) ** Processing: /home/hornik/tmp/CRAN_recheck/lnmixsurv.Rcheck/vign_test/lnmixsurv/vignettes/parallel_computation_files/figure-html/unnamed-chunk-4-2.png 288x288 pixels, 3x8 bits/pixel, RGB Input IDAT size = 12122 bytes Input file size = 12212 bytes Trying: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 9526 zc = 9 zm = 8 zs = 1 f = 0 zc = 1 zm = 8 zs = 2 f = 0 zc = 9 zm = 8 zs = 3 f = 0 zc = 9 zm = 8 zs = 0 f = 5 zc = 9 zm = 8 zs = 1 f = 5 zc = 1 zm = 8 zs = 2 f = 5 zc = 9 zm = 8 zs = 3 f = 5 Selecting parameters: zc = 9 zm = 8 zs = 0 f = 0 IDAT size = 9526 Output IDAT size = 9526 bytes (2596 bytes decrease) Output file size = 9604 bytes (2608 bytes = 21.36% decrease) --- finished re-building ‘parallel_computation.Rmd’ SUMMARY: processing the following file failed: ‘intercept_only.Rmd’ Error: Vignette re-building failed. Execution halted Package: lnmixsurv Check: tests New result: ERROR Running ‘testthat.R’ [34s/34s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > library(lnmixsurv) Loading required package: parsnip Loading required package: survival Loading required package: ggplot2 > > test_check("lnmixsurv") [ FAIL 2 | WARN 0 | SKIP 6 | PASS 50 ] ══ Skipped tests (6) ═══════════════════════════════════════════════════════════ • On CRAN (6): 'test-join_empirical_hazard.R:6:3', 'test-plot_fit_on_data.R:6:3', 'test-plot_fit_on_data.R:34:3', 'test-survival_ln_mixture-print-summary.R:3:3', 'test-survival_ln_mixture_em-methods.R:25:3', 'test-survival_ln_mixture_em-print-summary.R:3:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-plot_fit_on_data.R:20:3'): plot_fit_on_data works for Bayesian model with intercept only ── Error in `survfit.formula(form, data)`: Response must be a survival object Backtrace: ▆ 1. └─testthat::expect_snapshot(...) at test-plot_fit_on_data.R:20:3 2. └─rlang::cnd_signal(state$error) ── Error ('test-plot_fit_on_data.R:42:3'): plot_fit_on_data works for EM model with intercept only ── Error in `survfit.formula(form, data)`: Response must be a survival object Backtrace: ▆ 1. └─testthat::expect_snapshot(plot_fit_on_data(mod, data_model, type = "survival")$preds) at test-plot_fit_on_data.R:42:3 2. └─rlang::cnd_signal(state$error) [ FAIL 2 | WARN 0 | SKIP 6 | PASS 50 ] Error: Test failures Execution halted Package: MASS Check: differences from ‘MASS-Ex.Rout’ to ‘MASS-Ex.Rout.save’ New result: NOTE 2691c2691 < Number of Newton-Raphson Iterations: 8 --- > Number of Newton-Raphson Iterations: 7 Package: model4you Check: examples New result: ERROR Running examples in ‘model4you-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: coeftable.survreg > ### Title: Table of coefficients for survreg model > ### Aliases: coeftable.survreg > > ### ** Examples > > if(require("survival") & require("TH.data")) { + ## Load data + data(GBSG2, package = "TH.data") + + ## Weibull model + bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE) + + ## Coefficient table + grid.newpage() + coeftable.survreg(bmod) + + ## partitioned model + tr <- pmtree(bmod) + + ## plot + plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot, + confint = TRUE, coeftable = coeftable.survreg)) + } Loading required package: survival Loading required package: TH.data Loading required package: MASS Attaching package: ‘TH.data’ The following object is masked from ‘package:MASS’: geyser No data given. I'm using data set GBSG2 from the current environment parent.frame(). Please check if that is what you want. Error in eval(substitute(subset), data, env) : object 'wterminals' not found Calls: plot ... eval -> -> model.frame.default -> eval -> eval Execution halted Package: model4you Check: tests New result: ERROR Running ‘test-pmodel-test.R’ [19s/19s] Running ‘test-pmodel.R’ [14s/14s] Comparing ‘test-pmodel.Rout’ to ‘test-pmodel.Rout.save’ ... OK Running ‘test-pmtree.R’ [3s/3s] Running the tests in ‘tests/test-pmtree.R’ failed. Complete output: > library("model4you") Loading required package: partykit Loading required package: grid Loading required package: libcoin Loading required package: mvtnorm > library("survival") > > ### survreg > set.seed(1) > data(GBSG2, package = "TH.data") > > ## base model > bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE) > survreg_plot(bmod) > grid.newpage() > coeftable.survreg(bmod) > > > ## partitioned model > tr <- pmtree(bmod) No data given. I'm using data set GBSG2 from the current environment parent.frame(). Please check if that is what you want. > plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot, + confint = TRUE)) Error in eval(substitute(subset), data, env) : object 'wterminals' not found Calls: plot ... eval -> -> model.frame.default -> eval -> eval Execution halted Package: My.stepwise Check: examples New result: ERROR Running examples in ‘My.stepwise-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: My.stepwise.coxph > ### Title: Stepwise Variable Selection Procedure for Cox's Proportional > ### Hazards Model and Cox's Model > ### Aliases: My.stepwise.coxph > > ### ** Examples > > ## Not run: > ##D The data 'lung' is available in the 'survival' package. > ## End(Not run) > > if (requireNamespace("survival", quietly = TRUE)) { + lung <- survival::lung + } > > names(lung) [1] "inst" "time" "status" "age" "sex" "ph.ecog" [7] "ph.karno" "pat.karno" "meal.cal" "wt.loss" > dim(lung) [1] 228 10 > my.data <- na.omit(lung) > dim(my.data) [1] 167 10 > head(my.data) inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss 2 3 455 2 68 1 0 90 90 1225 15 4 5 210 2 57 1 1 90 60 1150 11 6 12 1022 1 74 1 1 50 80 513 0 7 7 310 2 68 2 2 70 60 384 10 8 11 361 2 71 2 2 60 80 538 1 9 1 218 2 53 1 1 70 80 825 16 > my.data$status1 <- ifelse(my.data$status==2,1,0) > my.variable.list <- c("inst", "age", "sex", "ph.ecog", "ph.karno", "pat.karno") > My.stepwise.coxph(Time = "time", Status = "status1", variable.list = my.variable.list, + in.variable = c("meal.cal", "wt.loss"), data = my.data) # -------------------------------------------------------------------------------------------------- # Initial Model: Call: coxph(formula = Surv(time, status1) ~ meal.cal + wt.loss, data = data, method = "efron") n= 167, number of events= 120 coef exp(coef) se(coef) z Pr(>|z|) meal.cal -1.198e-04 9.999e-01 2.430e-04 -0.493 0.622 wt.loss -4.974e-05 1.000e+00 6.798e-03 -0.007 0.994 exp(coef) exp(-coef) lower .95 upper .95 meal.cal 0.9999 1 0.9994 1.000 wt.loss 1.0000 1 0.9867 1.013 Concordance= 0.534 (se = 0.031 ) Likelihood ratio test= 0.25 on 2 df, p=0.9 Wald test = 0.24 on 2 df, p=0.9 Score (logrank) test = 0.24 on 2 df, p=0.9 --------------- Variance Inflating Factor (VIF) --------------- Multicollinearity Problem: Variance Inflating Factor (VIF) is bigger than 10 (Continuous Variable) or is bigger than 2.5 (Categorical Variable) meal.cal wt.loss 1.027017 1.027017 Warning in min(anova.pvalue2, na.rm = TRUE) : no non-missing arguments to min; returning Inf Warning in min(anova.pvalue2, na.rm = TRUE) : no non-missing arguments to min; returning Inf # ================================================================================================== *** Stepwise Final Model (in.lr.test: sle = 0.15; variable selection restrict in vif = 999): Call: coxph(formula = Surv(time, status1) ~ meal.cal + wt.loss, data = data, method = "efron") n= 167, number of events= 120 coef exp(coef) se(coef) z Pr(>|z|) meal.cal -1.198e-04 9.999e-01 2.430e-04 -0.493 0.622 wt.loss -4.974e-05 1.000e+00 6.798e-03 -0.007 0.994 exp(coef) exp(-coef) lower .95 upper .95 meal.cal 0.9999 1 0.9994 1.000 wt.loss 1.0000 1 0.9867 1.013 Concordance= 0.534 (se = 0.031 ) Likelihood ratio test= 0.25 on 2 df, p=0.9 Wald test = 0.24 on 2 df, p=0.9 Score (logrank) test = 0.24 on 2 df, p=0.9 > > my.variable.list <- c("inst", "age", "sex", "ph.ecog", "ph.karno", "pat.karno", "meal.cal", + "wt.loss") > My.stepwise.coxph(Time = "time", Status = "status1", variable.list = my.variable.list, + data = my.data, sle = 0.25, sls = 0.25) Error in coxph(as.formula(paste("Surv(", Time, ", ", Status, ") ~ ", paste(in.variable, : Response must be a survival object Calls: My.stepwise.coxph -> coxph Execution halted Package: PHInfiniteEstimates Check: examples New result: ERROR Running examples in ‘PHInfiniteEstimates-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: simultaneouscoverage > ### Title: Calculate simultaneous coverage of pointwise confidence > ### intervals. > ### Aliases: simultaneouscoverage > > ### ** Examples > > simultaneouscoverage(1000,20) Error in Surv(tt, status) : Time variable is not numeric Calls: simultaneouscoverage ... -> model.frame.default -> eval -> eval -> Surv Execution halted Package: reportRmd Check: examples New result: ERROR Running examples in ‘reportRmd-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: rm_survtime > ### Title: Display survival rates and events for specified times > ### Aliases: rm_survtime > > ### ** Examples > > # Kaplan-Mieir survival probabilities with time displayed in years > data("pembrolizumab") > rm_survtime(data=pembrolizumab,time='os_time',status='os_status', + strata="cohort",type='KM',survtimes=seq(12,72,12), + survtimesLbls=seq(1,6,1), + survtimeunit='years')
Time (years) At Risk Events Censored Survival Rate (95% CI)
Overall 94
12 48 44 2 0.53 (0.44, 0.64)
24 24 17 7 0.33 (0.24, 0.44)
36 0 3 21 0.28 (0.20, 0.40)
A 16
12 6 10 0 0.38 (0.20, 0.71)
24 1 2 3 0.23 (0.09, 0.59)
36 0 0 1 0.23 (0.09, 0.59)
B 18
12 5 12 1 0.32 (0.16, 0.64)
24 0 4 1 0.06 (9.6e-03, 0.42)
C 18
12 11 7 0 0.61 (0.42, 0.88)
24 8 3 0 0.44 (0.27, 0.74)
36 0 2 6 0.31 (0.15, 0.64)
D 12
12 8 4 0 0.67 (0.45, 0.99)
24 6 0 2 0.67 (0.45, 0.99)
36 0 0 6 0.67 (0.45, 0.99)
E 30
12 18 11 1 0.63 (0.48, 0.83)
24 9 8 1 0.34 (0.20, 0.57)
36 0 1 8 0.29 (0.15, 0.53)
> > # Cox Proportional Hazards survivial probabilities > rm_survtime(data=pembrolizumab,time='os_time',status='os_status', + strata="cohort",type='PH',survtimes=seq(12,72,12),survtimeunit='months') Error in .subset2(x, i, exact = exact) : invalid subscript type 'closure' Calls: rm_survtime ... [ -> [.data.frame -> [[ -> [[.data.frame -> Execution halted Package: reportRmd Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘reportRmd.Rmd’ using rmarkdown Quitting from lines 278-281 [unnamed-chunk-22] (reportRmd.Rmd) Error: processing vignette 'reportRmd.Rmd' failed with diagnostics: invalid subscript type 'closure' --- failed re-building ‘reportRmd.Rmd’ SUMMARY: processing the following file failed: ‘reportRmd.Rmd’ Error: Vignette re-building failed. Execution halted Package: RESI Check: examples New result: ERROR Running examples in ‘RESI-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: resi > ### Title: Robust Effect Size Index (RESI) point and interval estimation > ### for models > ### Aliases: resi resi.default resi.glm resi.lm resi.nls resi.survreg > ### resi.coxph resi.hurdle resi.zeroinfl resi.geeglm resi.gee resi.lme > ### resi.lmerMod > > ### ** Examples > > ## for timing purposes, a small number of bootstrap replicates is used in the > ## examples. Run them with a higher or default `nboot` argument for better performance > > ## RESI on a linear model > # fit linear model > mod = lm(charges ~ region * age + bmi + sex, data = RESI::insurance) > > # run resi on fitted model with desired number of bootstrap replicates > # store bootstrap results for calculating different CIs later > resi_obj = resi(mod, nboot = 50, store.boot = TRUE) > # print output > resi_obj Analysis of effect sizes based on RESI: Confidence level = 0.05 Call: lm(formula = charges ~ region * age + bmi + sex, data = RESI::insurance) Coefficient Table Estimate Std. Error t value Pr(>|t|) RESI 2.5% (Intercept) -5359.4352 2175.9439 -2.4630 0.0139 -0.0673 -0.1187 regionnorthwest -2339.4433 2395.1507 -0.9767 0.3289 -0.0267 -0.0726 regionsoutheast -3230.8512 2643.1099 -1.2224 0.2218 -0.0334 -0.0943 regionsouthwest -232.4839 2574.2823 -0.0903 0.9281 -0.0025 -0.0457 age 220.3325 40.2091 5.4797 0.0000 0.1497 0.0985 bmi 323.7725 58.0849 5.5741 0.0000 0.1523 0.1121 sexmale 1328.0215 621.7421 2.1360 0.0329 0.0584 0.0005 regionnorthwest:age 34.9040 57.2364 0.6098 0.5421 0.0167 -0.0308 regionsoutheast:age 83.6359 63.3258 1.3207 0.1868 0.0361 -0.0180 regionsouthwest:age -33.6290 61.4065 -0.5476 0.5840 -0.0150 -0.0472 97.5% (Intercept) -0.0211 regionnorthwest 0.0228 regionsoutheast 0.0156 regionsouthwest 0.0393 age 0.1992 bmi 0.1921 sexmale 0.1092 regionnorthwest:age 0.0775 regionsoutheast:age 0.0892 regionsouthwest:age 0.0300 Analysis of Deviance Table (Type II tests) Response: charges Df F Pr(>F) RESI 2.5% 97.5% region 3 1.5959 0.1886 0.0365 0.0000 0.1054 age 1 117.7046 0.0000 0.2951 0.2356 0.3409 bmi 1 31.0708 0.0000 0.1498 0.1087 0.1901 sex 1 4.5624 0.0329 0.0515 0.0000 0.1057 region:age 3 1.1167 0.3412 0.0161 0.0000 0.1058 Overall RESI comparing model to intercept-only model: Res.Df Df F Pr(>F) RESI 2.5% 97.5% 1 1328 9 20.2486 0 0.3595 0.3195 0.4109 Notes: 1. The RESI was calculated using a robust covariance estimator. 2. Confidence intervals (CIs) constructed using 50 non-parametric bootstraps. > > # fit a reduced model for comparison > mod_red = lm(charges ~ bmi, data = RESI::insurance) > > # running resi and including the reduced model will provide almost the exact same > # output as not including a reduced model. The difference is that the "overall" > # portion of the output will compare the full model to the reduced model. > # The "summary" and "anova" RESI estimates will be the same. (The bootstrapped > # confidence intervals may differ.) > resi(model.full = mod, model.reduced = mod_red, nboot = 10) Analysis of effect sizes based on RESI: Confidence level = 0.05 Full Model:lm(formula = charges ~ region * age + bmi + sex, data = RESI::insurance) Reduced Model:lm(formula = charges ~ bmi, data = RESI::insurance) Coefficient Table Estimate Std. Error t value Pr(>|t|) RESI 2.5% (Intercept) -5359.4352 2175.9439 -2.4630 0.0139 -0.0673 -0.0857 regionnorthwest -2339.4433 2395.1507 -0.9767 0.3289 -0.0267 -0.0834 regionsoutheast -3230.8512 2643.1099 -1.2224 0.2218 -0.0334 -0.0947 regionsouthwest -232.4839 2574.2823 -0.0903 0.9281 -0.0025 -0.0407 age 220.3325 40.2091 5.4797 0.0000 0.1497 0.1111 bmi 323.7725 58.0849 5.5741 0.0000 0.1523 0.1316 sexmale 1328.0215 621.7421 2.1360 0.0329 0.0584 0.0309 regionnorthwest:age 34.9040 57.2364 0.6098 0.5421 0.0167 -0.0059 regionsoutheast:age 83.6359 63.3258 1.3207 0.1868 0.0361 0.0147 regionsouthwest:age -33.6290 61.4065 -0.5476 0.5840 -0.0150 -0.0355 97.5% (Intercept) -0.0252 regionnorthwest -0.0045 regionsoutheast -0.0092 regionsouthwest 0.0275 age 0.1852 bmi 0.1673 sexmale 0.1139 regionnorthwest:age 0.0789 regionsoutheast:age 0.1012 regionsouthwest:age 0.0279 Analysis of Deviance Table (Type II tests) Response: charges Df F Pr(>F) RESI 2.5% 97.5% region 3 1.5959 0.1886 0.0365 0.0000 0.0627 age 1 117.7046 0.0000 0.2951 0.2584 0.3347 bmi 1 31.0708 0.0000 0.1498 0.1287 0.1650 sex 1 4.5624 0.0329 0.0515 0.0144 0.1104 region:age 3 1.1167 0.3412 0.0161 0.0026 0.1073 Overall RESI comparing full model to reduced model: Res.Df Df F Pr(>F) RESI 2.5% 97.5% 1 1328 8 15.9113 0 0.2983 0.2686 0.3521 Notes: 1. The RESI was calculated using a robust covariance estimator. 2. Confidence intervals (CIs) constructed using 10 non-parametric bootstraps. > > # used stored bootstrap results to get a different alpha-level confidence interval > summary(resi_obj, alpha = c(0.01, 0.1)) Analysis of effect sizes based on RESI: Confidence level = 0.01 0.1 Call: lm(formula = charges ~ region * age + bmi + sex, data = RESI::insurance) Coefficient Table Estimate Std. Error t value Pr(>|t|) RESI 0.5% (Intercept) -5359.4352 2175.9439 -2.4630 0.0139 -0.0673 -0.1264 regionnorthwest -2339.4433 2395.1507 -0.9767 0.3289 -0.0267 -0.0793 regionsoutheast -3230.8512 2643.1099 -1.2224 0.2218 -0.0334 -0.0968 regionsouthwest -232.4839 2574.2823 -0.0903 0.9281 -0.0025 -0.0543 age 220.3325 40.2091 5.4797 0.0000 0.1497 0.0965 bmi 323.7725 58.0849 5.5741 0.0000 0.1523 0.1092 sexmale 1328.0215 621.7421 2.1360 0.0329 0.0584 -0.0178 regionnorthwest:age 34.9040 57.2364 0.6098 0.5421 0.0167 -0.0506 regionsoutheast:age 83.6359 63.3258 1.3207 0.1868 0.0361 -0.0205 regionsouthwest:age -33.6290 61.4065 -0.5476 0.5840 -0.0150 -0.0631 5% 95% 99.5% (Intercept) -0.1059 -0.0241 -0.0082 regionnorthwest -0.0679 0.0159 0.0414 regionsoutheast -0.0830 0.0115 0.0200 regionsouthwest -0.0340 0.0312 0.0462 age 0.1049 0.1900 0.2043 bmi 0.1195 0.1896 0.2033 sexmale 0.0090 0.1053 0.1230 regionnorthwest:age -0.0279 0.0645 0.0809 regionsoutheast:age -0.0080 0.0840 0.1033 regionsouthwest:age -0.0459 0.0256 0.0396 > car::Anova(resi_obj, alpha = c(0.01, 0.1)) Df F Pr(>F) RESI 0.5% 5% 95% 99.5% region 3 1.5959 0.18856 0.036480 0.00000 0.00000 0.09931 0.12299 age 1 117.7046 0.00000 0.295111 0.22529 0.23913 0.33668 0.35655 bmi 1 31.0708 0.00000 0.149798 0.10566 0.11635 0.18760 0.20144 sex 1 4.5624 0.03286 0.051549 0.00000 0.00000 0.10164 0.11985 region:age 3 1.1167 0.34115 0.016056 0.00000 0.00000 0.10546 0.11277 > > # the result of resi, as well as the summary or Anova of a `resi` object can be plotted > # if the resi object was created with the store.boot = `TRUE` option, any alpha > # can be specified > plot(resi_obj, alpha = 0.01) > # if the variable names on the y-axis are too long, you can reduce their size with > # the ycex.axis argument (or use regular common solutions like changing the margins) > plot(resi_obj, alpha = 0.01, ycex.axis = 0.5) > > # for some model types and formula structures, data argument is required > if(requireNamespace("splines")){ + # fit logistic regression model with splines + mod = glm(smoker ~ splines::ns(age, df = 3) + region, data = RESI::insurance, + family = "binomial") + + # specify additional arguments to the variance-covariance function via vcov.args + resi_obj = resi(mod, data = RESI::insurance, alpha = 0.01, + vcov.args = list(type = "HC0"), nboot = 25) + summary(resi_obj) + car::Anova(resi_obj)} Loading required namespace: splines Analysis of Deviance Table (Type II tests) Response: smoker Df Chisq Pr(>Chisq) RESI 0.5% 99.5% splines::ns(age, df = 3) 3 1.4735 0.68841 0.000000 0 0.094972 region 3 7.2960 0.06304 0.056663 0 0.142062 > > > ## RESI on a survival model with alternate Z2S > if(requireNamespace("survival")){ + # fit coxph model on example data from survival package + # Note: for survival models, you need to specify robust variance in the model + # creation. resi will ignore the vcovfunc argument for this reason. + mod.coxph = survival::coxph(survival::Surv(time, status) ~ age + sex + wt.loss, + data=survival::lung, robust = TRUE) + + # run resi on the model + # to use the alternative Z to RESI formula (which is equal in absolute value to the + # chi-square to RESI (S) formula), specify unbiased = FALSE. + resi(mod.coxph, data = survival::lung, unbiased = FALSE, nboot = 10)} Loading required namespace: survival Error in resi.default(model.full = model.full, model.reduced = NULL, data = data, : Bootstrapping failed. Run resi_pe for point estimates Calls: resi -> resi.coxph -> resi.default Execution halted Package: RESI Check: tests New result: ERROR Running ‘testthat.R’ [63s/63s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > library(RESI) > > test_check("RESI") Loading required package: carData Loading required namespace: splines Loading required namespace: survival Loading required namespace: pscl Loading required namespace: gee Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 running glm to get initial regression estimate (Intercept) diagnosesevere drugstandard time -0.08759224 -1.31391092 0.05960381 1.49985707 drugstandard:time -1.01744498 Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 running glm to get initial regression estimate (Intercept) 0.0902573 Loading required namespace: geepack Loading required namespace: lme4 boundary (singular) fit: see help('isSingular') Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27 running glm to get initial regression estimate (Intercept) diagnosesevere drugstandard time -0.08759224 -1.31391092 0.05960381 1.49985707 drugstandard:time -1.01744498 Updating model fit failed. Try running with providing data argument Updating model fit failed. Try running with providing data argument [ FAIL 8 | WARN 0 | SKIP 0 | PASS 44 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-resi.R:142:3'): Specifying non-allowed vcov produces warning ─── Error in `resi.default(model.full = model.full, model.reduced = model.reduced, data = data, anova = anova, coefficients = coefficients, overall = overall, nboot = nboot, vcovfunc = vcov, store.boot = store.boot, Anova.args = Anova.args, unbiased = unbiased, alpha = alpha, boot.method = "nonparam", parallel = parallel, ncpus = ncpus, ...)`: Bootstrapping failed. Run resi_pe for point estimates Backtrace: ▆ 1. ├─testthat::expect_warning(...) at test-resi.R:142:3 2. │ └─testthat:::expect_condition_matching(...) 3. │ └─testthat:::quasi_capture(...) 4. │ ├─testthat (local) .capture(...) 5. │ │ └─base::withCallingHandlers(...) 6. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 7. ├─RESI::resi(mod.surv, data = data.surv, nboot = 1, vcovfunc = sandwich::vcovHC) 8. └─RESI:::resi.survreg(mod.surv, data = data.surv, nboot = 1, vcovfunc = sandwich::vcovHC) 9. └─RESI:::resi.default(...) ── Error ('test-resi.R:192:3'): resi produces the correct estimates ──────────── Error in `resi.default(model.full = model.full, model.reduced = model.reduced, data = data, anova = anova, coefficients = coefficients, overall = overall, nboot = nboot, vcovfunc = vcov, store.boot = store.boot, Anova.args = Anova.args, unbiased = unbiased, alpha = alpha, boot.method = "nonparam", parallel = parallel, ncpus = ncpus, ...)`: Bootstrapping failed. Run resi_pe for point estimates Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-resi.R:192:3 2. │ └─testthat::quasi_label(enquo(object), label, arg = "object") 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. ├─base::unname(resi(mod.surv, nboot = 1, data = data.surv)$estimates) 5. ├─RESI::resi(mod.surv, nboot = 1, data = data.surv) 6. └─RESI:::resi.survreg(mod.surv, nboot = 1, data = data.surv) 7. └─RESI:::resi.default(...) ── Error ('test-resi.R:244:3'): RESI estimates are in between the confidence limits ── Error in `resi.default(model.full = model.full, model.reduced = NULL, data = data, anova = anova, coefficients = coefficients, overall = overall, nboot = nboot, vcovfunc = vcov, store.boot = store.boot, Anova.args = Anova.args, unbiased = unbiased, alpha = alpha, boot.method = "nonparam", parallel = parallel, ncpus = ncpus, skip.red = TRUE, ...)`: Bootstrapping failed. Run resi_pe for point estimates Backtrace: ▆ 1. ├─RESI::resi(mod.coxph, data = data.surv, nboot = 500) at test-resi.R:244:3 2. └─RESI:::resi.coxph(mod.coxph, data = data.surv, nboot = 500) 3. └─RESI:::resi.default(...) ── Error ('test-resi.R:280:3'): unbiased = FALSE returns same abs. RESI as Chi-sq/F ── Error in `resi.default(model.full = model.full, model.reduced = model.reduced, data = data, anova = anova, coefficients = coefficients, overall = overall, nboot = nboot, vcovfunc = vcov, store.boot = store.boot, Anova.args = Anova.args, unbiased = unbiased, alpha = alpha, boot.method = "nonparam", parallel = parallel, ncpus = ncpus, ...)`: Bootstrapping failed. Run resi_pe for point estimates Backtrace: ▆ 1. ├─RESI::resi(mod.surv, data = data.surv, nboot = 1, unbiased = FALSE) at test-resi.R:280:3 2. └─RESI:::resi.survreg(mod.surv, data = data.surv, nboot = 1, unbiased = FALSE) 3. └─RESI:::resi.default(...) ── Error ('test-resi.R:304:3'): Specifying a reduced model only changes overall output ── Error in `resi.default(model.full = model.full, model.reduced = model.reduced, data = data, anova = anova, coefficients = coefficients, overall = overall, nboot = nboot, vcovfunc = vcov, store.boot = store.boot, Anova.args = Anova.args, unbiased = unbiased, alpha = alpha, boot.method = "nonparam", parallel = parallel, ncpus = ncpus, ...)`: Bootstrapping failed. Run resi_pe for point estimates Backtrace: ▆ 1. ├─RESI::resi(mod.surv, data = data.surv, nboot = 1) at test-resi.R:304:3 2. └─RESI:::resi.survreg(mod.surv, data = data.surv, nboot = 1) 3. └─RESI:::resi.default(...) ── Error ('test-resi.R:322:3'): specifying additional Anova args works ───────── Error in `resi.default(model.full = model.full, model.reduced = model.reduced, data = data, anova = anova, coefficients = coefficients, overall = overall, nboot = nboot, vcovfunc = vcov, store.boot = store.boot, Anova.args = Anova.args, unbiased = unbiased, alpha = alpha, boot.method = "nonparam", parallel = parallel, ncpus = ncpus, ...)`: Bootstrapping failed. Run resi_pe for point estimates Backtrace: ▆ 1. ├─testthat::expect_false(...) at test-resi.R:322:3 2. │ └─testthat::quasi_label(enquo(object), label, arg = "object") 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. ├─RESI::resi(mod.surv, data = data.surv, nboot = 1, Anova.args = list(type = "3")) 5. └─RESI:::resi.survreg(mod.surv, data = data.surv, nboot = 1, Anova.args = list(type = "3")) 6. └─RESI:::resi.default(...) ── Error ('test-resi.R:343:3'): tibbles work ─────────────────────────────────── Error in `resi.default(model.full = model.full, model.reduced = model.reduced, data = data, anova = anova, coefficients = coefficients, overall = overall, nboot = nboot, vcovfunc = vcov, store.boot = store.boot, Anova.args = Anova.args, unbiased = unbiased, alpha = alpha, boot.method = "nonparam", parallel = parallel, ncpus = ncpus, ...)`: Bootstrapping failed. Run resi_pe for point estimates Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-resi.R:343:3 2. │ └─testthat::quasi_label(enquo(object), label, arg = "object") 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. ├─base::unname(resi(mod.surv.tib, nboot = 1, data = data.surv)$estimates) 5. ├─RESI::resi(mod.surv.tib, nboot = 1, data = data.surv) 6. └─RESI:::resi.survreg(mod.surv.tib, nboot = 1, data = data.surv) 7. └─RESI:::resi.default(...) ── Error ('test-resi.R:366:5'): intercept-only full model works and doesn't have overall element ── Error in `resi.default(model.full = model.full, model.reduced = model.reduced, data = data, anova = anova, coefficients = coefficients, overall = overall, nboot = nboot, vcovfunc = vcov, store.boot = store.boot, Anova.args = Anova.args, unbiased = unbiased, alpha = alpha, boot.method = "nonparam", parallel = parallel, ncpus = ncpus, ...)`: Bootstrapping failed. Run resi_pe for point estimates Backtrace: ▆ 1. ├─testthat::expect_true(...) at test-resi.R:366:5 2. │ └─testthat::quasi_label(enquo(object), label, arg = "object") 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. ├─RESI::resi(mod.surv.int, data = data.surv, nboot = 1, Anova.args = list(type = 3)) 5. └─RESI:::resi.survreg(...) 6. └─RESI:::resi.default(...) [ FAIL 8 | WARN 0 | SKIP 0 | PASS 44 ] Error: Test failures Execution halted Package: rineq Check: examples New result: ERROR Running examples in ‘rineq-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: contribution > ### Title: Function to decompose the Relative Concentration Index into its > ### components > ### Aliases: contribution > > ### ** Examples > > data(housing) > > ## Linear regression direct decomposition > fit.lm <- lm(bmi ~ sex + tenure + place + age,data = housing) > > # decompose relative concentration index > contrib.lm <- contribution(fit.lm, housing$income) > summary(contrib.lm) Overall CI: 0.121004 95% confidence interval: 0.1181791 0.123829 Decomposition: Contribution (%) Contribution (Abs) Elasticity residual 38.2448886 0.0462779 0.0000000 sexmale 70.3033532 0.0850699 0.2241433 tenureirregular -16.8438839 -0.0203818 0.0307882 tenureown_apartment 0.0751962 0.0000910 0.0018771 tenureown_house 1.3489119 0.0016322 0.0136284 tenurerent 6.6988511 0.0081059 0.0517000 placeurban 0.0671404 0.0000812 -0.0123887 age 0.1055426 0.0001277 0.0324067 Concentration Index lower 5% upper 5% Corrected residual NA NA NA no sexmale 0.3795336 0.3691540 0.3899131 no tenureirregular -0.6619993 -0.6980505 -0.6259481 no tenureown_apartment 0.0484737 0.0260028 0.0709447 no tenureown_house 0.1197676 0.0926766 0.1468586 no tenurerent 0.1567869 0.1440879 0.1694859 no placeurban -0.0065578 -0.0188208 0.0057052 no age 0.0039409 -0.0000583 0.0079401 no > plot(contrib.lm, decreasing = FALSE, horiz = TRUE) > > > # GLM: Decomposition based on predicted outcome > fit.logit <-glm(high.bmi ~ sex + tenure + place + age, data = housing) > > contrib.logit <- contribution(fit.logit, housing$income) > summary(contrib.logit) Overall CI: 0.2502025 95% confidence interval: 0.2429066 0.2574983 Decomposition: Contribution (%) Contribution (Abs) Elasticity residual 0.0000000 0.0000000 0.0000000 sexmale 108.4563232 0.2713604 0.7149839 tenureirregular -17.2872968 -0.0432532 0.0653373 tenureown_apartment 0.1140275 0.0002853 0.0058857 tenureown_house 1.4916426 0.0037321 0.0311614 tenurerent 7.0608518 0.0176664 0.1126780 placeurban 0.0573415 0.0001435 -0.0218778 age 0.1071102 0.0002680 0.0680031 Concentration Index lower 5% upper 5% Corrected residual NA NA NA no sexmale 0.3795336 0.3691540 0.3899131 no tenureirregular -0.6619993 -0.6980505 -0.6259481 no tenureown_apartment 0.0484737 0.0260028 0.0709447 no tenureown_house 0.1197676 0.0926766 0.1468586 no tenurerent 0.1567869 0.1440879 0.1694859 no placeurban -0.0065578 -0.0188208 0.0057052 no age 0.0039409 -0.0000583 0.0079401 no > plot(contrib.logit, decreasing = FALSE,horiz = TRUE) > > > # GLM probit: Decomposition based on predicted outcome > fit.probit <-glm(high.bmi ~ sex + tenure + place + age, data = housing, + family = binomial(link = probit)) > > # binary, set type to 'CIw' > contrib.probit <- contribution(fit.probit, housing$income, type = "CIw") > summary(contrib.probit) Overall CI: -0.26355 95% confidence interval: -0.2718336 -0.2552664 (based on a corrected value) Decomposition: Contribution (%) Contribution (Abs) Elasticity residual 272.0517695 -0.7169924 0.0000000 sexmale -179.3760257 0.4727455 0.6287769 tenureirregular 25.5830437 -0.0674241 0.0866431 tenureown_apartment -0.1800413 0.0004745 0.0077997 tenureown_house -2.1688445 0.0057160 0.0404237 tenurerent -15.7773222 0.0415811 0.1471374 placeurban -0.1352142 0.0003564 -0.0293442 age 0.0026347 -0.0000069 0.0893297 Concentration Index lower 5% upper 5% Corrected residual NA NA NA no sexmale 0.7518494 0.7312877 0.7724110 no tenureirregular -0.7781818 -0.8205601 -0.7358035 no tenureown_apartment 0.0608355 0.0326340 0.0890370 no tenureown_house 0.1414021 0.1094175 0.1733867 no tenurerent 0.2826007 0.2597114 0.3054901 no placeurban -0.0121440 -0.0348533 0.0105652 no age -0.0000777 -0.0001566 0.0000012 no > plot(contrib.probit, decreasing = FALSE,horiz = TRUE) > > > # Marginal effects probit using package 'mfx': Decomposition based on predicted outcome > fit.mfx <-mfx::probitmfx(high.bmi ~ sex + tenure + place + age, data = housing) > > contrib.mfx <- contribution(fit.mfx, housing$income, type = "CIw") > summary(contrib.mfx, type="CIw") Overall CI: 0.6906082 95% confidence interval: 0.6694697 0.7117467 Decomposition: Contribution (%) Contribution (Abs) Elasticity residual 22.2833787 0.1538908 0.0000000 sexmale 80.4392036 0.5555197 0.7388710 tenureirregular -12.5529543 -0.0866917 0.1114029 tenureown_apartment 0.0984600 0.0006800 0.0111772 tenureown_house 1.1582469 0.0079989 0.0565688 tenurerent 8.5011533 0.0587097 0.2077477 placeurban 0.0739535 0.0005107 -0.0420559 age -0.0014417 -0.0000100 0.1280922 Concentration Index lower 5% upper 5% Corrected residual NA NA NA no sexmale 0.7518494 0.7312877 0.7724110 no tenureirregular -0.7781818 -0.8205601 -0.7358035 no tenureown_apartment 0.0608355 0.0326340 0.0890370 no tenureown_house 0.1414021 0.1094175 0.1733867 no tenurerent 0.2826007 0.2597114 0.3054901 no placeurban -0.0121440 -0.0348533 0.0105652 no age -0.0000777 -0.0001566 0.0000012 no > plot(contrib.mfx, decreasing = FALSE, horiz = TRUE) > > > # package survey svy > des = survey::svydesign(~1, data= housing, weights = rep(1, NROW(housing))) > fit.svy = survey::svyglm(bmi ~ tenure+height+weight, design = des) > contrib.svy = contribution(fit.svy, housing$income) > > > # adopted from the `coxph` example in survival package > testcph <- data.frame(time = c(4,3,1,1,2,2,3), + status = c(1,1,1,0,1,1,0), + x = c(0,2,1,1,1,0,0), + sex = c(0,0,0,0,1,1,1), + income = c(100,50, 20, 20, 50, 60,100)) > > # Fit a stratified model > fit.coxph = survival::coxph(survival::Surv(time, status) ~ x + survival::strata(sex), testcph) > contrib.coxph = contribution(fit.coxph, testcph$income) Error in model.matrix(object)[, names(object$coefficients)][, -1, drop = F] : incorrect number of dimensions Calls: contribution -> contribution.coxph Execution halted Package: sandwich Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘sandwich-CL.Rnw’ using Sweave Loading required package: geepack Loading required package: lattice Loading required package: lme4 Loading required package: Matrix Loading required package: lmtest Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric Loading required package: multiwayvcov Loading required package: pcse Attaching package: ‘pcse’ The following object is masked from ‘package:sandwich’: vcovPC Loading required package: plm Loading required package: pscl Classes and Methods for R originally developed in the Political Science Computational Laboratory Department of Political Science Stanford University (2002-2015), by and under the direction of Simon Jackman. hurdle and zeroinfl functions by Achim Zeileis. --- finished re-building ‘sandwich-CL.Rnw’ --- re-building ‘sandwich-OOP.Rnw’ using Sweave Loading required package: AER Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival Loading required package: MASS Error: processing vignette 'sandwich-OOP.Rnw' failed with diagnostics: chunk 10 Error in model.matrix.default(terms(x), mf) : model frame and formula mismatch in model.matrix() --- failed re-building ‘sandwich-OOP.Rnw’ --- re-building ‘sandwich.Rnw’ using Sweave Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric Loading required package: strucchange Loading required package: lmtest Loading required package: scatterplot3d --- finished re-building ‘sandwich.Rnw’ SUMMARY: processing the following file failed: ‘sandwich-OOP.Rnw’ Error: Vignette re-building failed. Execution halted Package: sandwich Check: running R code from vignettes New result: ERROR Errors in running code in vignettes: when running code in ‘sandwich-OOP.Rnw’ ... > options(prompt = "R> ", continue = "+ ") > if (!require("AER")) tobit <- glm Loading required package: AER Loading required package: car Loading required package: carData Loading required package: lmtest Loading required package: zoo Attaching package: ‘zoo’ The following objects are masked from ‘package:base’: as.Date, as.Date.numeric Loading required package: sandwich Loading required package: survival > if (!require("MASS")) glm.nb <- glm Loading required package: MASS > if (!require("lmtest")) coeftest <- function(object, + ...) summary(object)$coefficients > warn <- if (require("AER") & require("MASS") & require("lmtest")) "" else "{\\\\large\\\\bf\\\\color{Red}\n Not all required packages were availab ..." ... [TRUNCATED] > par(mar = rep(0, 4)) > plot(0, 0, xlim = c(0, 85), ylim = c(0, 110), type = "n", + axes = FALSE, xlab = "", ylab = "") > lgrey <- grey(0.88) > dgrey <- grey(0.75) > rect(45, 90, 70, 110, lwd = 2, col = dgrey) > rect(20, 40, 40, 60, col = lgrey) > rect(30, 40, 40, 60, col = dgrey) > rect(20, 40, 40, 60, lwd = 2) > rect(5, 0, 20, 20, lwd = 2, col = lgrey) > rect(22.5, 0, 37.5, 20, lwd = 2, col = lgrey) > rect(40, 0, 55, 20, lwd = 2, col = lgrey) > rect(40, 0, 55, 20, lwd = 2, col = lgrey) > rect(60, 0, 80, 20, col = lgrey) > rect(70, 0, 80, 20, col = dgrey) > rect(60, 0, 80, 20, lwd = 2) > text(57.5, 100, "fitted model object\n(class: foo)") > text(25, 50, "estfun") > text(35, 50, "foo") > text(12.5, 10, "meatHC") > text(30, 10, "meatHAC") > text(47.5, 10, "meat") > text(65, 10, "bread") > text(75, 10, "foo") > arrows(57.5, 89, 70, 21, lwd = 1.5, length = 0.15, + angle = 20) > arrows(57.5, 89, 30, 61, lwd = 1.5, length = 0.15, + angle = 20) > arrows(30, 39, 30, 21, lwd = 1.5, length = 0.15, angle = 20) > arrows(30, 39, 12.5, 21, lwd = 1.5, length = 0.15, + angle = 20) > arrows(30, 39, 47.5, 21, lwd = 1.5, length = 0.15, + angle = 20) > suppressWarnings(RNGversion("3.5.0")) > set.seed(123) > x <- rnorm(250) > y <- rnbinom(250, mu = exp(1 + x), size = 1) > fm_pois <- glm(y ~ x + I(x^2), family = poisson) > coeftest(fm_pois) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.063268 0.041357 25.7094 < 2e-16 *** x 0.996072 0.053534 18.6062 < 2e-16 *** I(x^2) -0.049124 0.023146 -2.1223 0.03381 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > coeftest(fm_pois, vcov = sandwich) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.063268 0.083776 12.6918 <2e-16 *** x 0.996072 0.105217 9.4668 <2e-16 *** I(x^2) -0.049124 0.036284 -1.3539 0.1758 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > fm_qpois <- glm(y ~ x + I(x^2), family = quasipoisson) > coeftest(fm_qpois) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.063268 0.090435 11.7572 <2e-16 *** x 0.996072 0.117063 8.5088 <2e-16 *** I(x^2) -0.049124 0.050613 -0.9706 0.3318 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > fm_nbin <- glm.nb(y ~ x + I(x^2)) > coeftest(fm_nbin) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.066057 0.088574 12.0358 <2e-16 *** x 0.999616 0.094894 10.5340 <2e-16 *** I(x^2) -0.052652 0.064883 -0.8115 0.4171 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > if (require("AER")) { + library("AER") + data("Affairs", package = "AER") + fm_tobit <- tobit(affairs ~ age + yearsmarried + religiousne .... [TRUNCATED] > coeftest(fm_tobit) z test of coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 8.174197 2.741446 2.9817 0.002866 ** age -0.179333 0.079093 -2.2674 0.023368 * yearsmarried 0.554142 0.134518 4.1195 3.798e-05 *** religiousness -1.686220 0.403752 -4.1764 2.962e-05 *** occupation 0.326053 0.254425 1.2815 0.200007 rating -2.284973 0.407828 -5.6028 2.109e-08 *** Log(scale) 2.109859 0.067098 31.4444 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > coeftest(fm_tobit, vcov = sandwich) When sourcing ‘sandwich-OOP.R’: Error: model frame and formula mismatch in model.matrix() Execution halted ‘sandwich-CL.Rnw’... [6s/6s] OK ‘sandwich-OOP.Rnw’... failed ‘sandwich.Rnw’... [1s/1s] OK Package: SurvHiDim Check: examples New result: ERROR Running examples in ‘SurvHiDim-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: multicoxa > ### Title: High dimensional multivariate cox proportional hazard data > ### analysis > ### Aliases: multicoxa > > ### ** Examples > > ## > multicoxa(m=15,n=18,OS="os",event="death",data=hnscc) Error in get(Surv) : invalid first argument Calls: multicoxa ... -> model.frame.default -> eval -> eval -> Surv -> get Execution halted Package: tern Check: tests New result: ERROR Running ‘testthat.R’ [164s/166s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > pkg_name <- "tern" > library(testthat) > test_check(pkg_name, reporter = ParallelProgressReporter$new()) Loading required package: tern Loading required package: rtables Loading required package: formatters Attaching package: 'formatters' The following object is masked from 'package:base': %||% Loading required package: magrittr Attaching package: 'magrittr' The following objects are masked from 'package:testthat': equals, is_less_than, not Attaching package: 'rtables' The following object is masked from 'package:utils': str Registered S3 method overwritten by 'tern': method from tidy.glm broom Attaching package: 'dplyr' The following object is masked from 'package:testthat': matches The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union ✔ | F W S OK | Context ⠋ [ FAIL 0 | WARN 0 | SKIP 0 | PASS 0 ] Starting up... ✔ | 7 7 | abnormal [1.4s] ✔ | 5 8 | abnormal_by_baseline ✔ | 3 4 | abnormal_by_marked [5.1s] ✔ | 2 3 | abnormal_by_worst_grade ✔ | 8 12 | abnormal_by_worst_grade_worsen [1.4s] ✔ | 31 47 | analyze_variables [4.8s] ✔ | 7 8 | analyze_vars_in_cols [4.5s] ✔ | 2 6 | bland-altman ✔ | 16 | combination_function ✔ | 9 17 | compare_variables ✔ | 1 3 | control_logistic ✔ | 1 4 | control_step ✔ | 3 11 | control_survival ✔ | 6 6 | count_cumulative ✔ | 5 5 | count_missed_doses ✔ | 8 15 | count_occurrences [1.3s] ✔ | 13 15 | count_occurrences_by_grade [2.7s] ✔ | 4 4 | count_patients_events_in_cols ✔ | 6 6 | count_patients_with_event ✔ | 8 8 | count_patients_with_flags [1.4s] ✔ | 10 10 | count_values skipping term strata(ecog.ps) ✔ | 5 11 | coxph ✖ | 1 24 49 | coxreg [1.9s] ──────────────────────────────────────────────────────────────────────────────── Failure ('test-coxreg.R:563:3'): fit_coxreg_multivar works correctly also without treatment arm stats::formula(result$mod) (`actual`) not equal to survival::Surv(time, status) ~ 1 + covar1 + covar2 (`expected`). `actual`: `Surv(time, status) ~ 1 + covar1 + covar2` `expected`: `survival::Surv(time, status) ~ 1 + covar1 + covar2` ──────────────────────────────────────────────────────────────────────────────── ✔ | 2 13 | decorate_grob [1.6s] ✔ | 5 5 | desctools_binom_diff ✔ | 5 12 | df_explicit_na ✔ | 3 7 | estimate_multinom_rsp ✔ | 17 17 | estimate_proportion [1.0s] ✔ | 3 13 | fit_rsp_step ✔ | 3 9 | fit_survival_step ✔ | 21 33 | formatting_functions ✔ | 3 18 | g_forest [8.2s] ✔ | 9 18 | g_km [10.9s] ✔ | 6 11 | g_lineplot [7.9s] ✔ | 4 10 | g_step [1.0s] ✔ | 2 2 | g_waterfall ✔ | 5 1 | h_adsl_adlb_merge_using_worst_flag ✔ | 1 1 | h_format_row ✔ | 5 5 | h_incidence_rate ✔ | 13 21 | h_km ✔ | 16 38 | h_logistic_regression ✔ | 7 7 | h_map_for_count_abnormal ✔ | 2 2 | h_pkparam_sort ✔ | 5 5 | h_response_biomarkers_subgroups ✔ | 14 15 | h_response_subgroups ✔ | 3 5 | h_stack_by_baskets ✔ | 8 43 | h_step ✔ | 4 8 | h_survival_biomarkers_subgroups ✔ | 16 17 | h_survival_duration_subgroups ✔ | 2 0 | imputation_rule ✔ | 8 12 | incidence_rate ✔ | 7 | individual_patient_plot ✔ | 6 21 | logistic_regression [4.0s] ✔ | 7 19 | make_afun ✔ | 7 8 | odds_ratio ✔ | 11 19 | prop_diff ✔ | 7 14 | prune_occurrences [4.2s] ✔ | 6 10 | response_biomarkers_subgroups [3.4s] ✔ | 14 16 | response_subgroups [8.7s] ✔ | 5 8 | rtables_access [2.6s] ✔ | 5 8 | score_occurrences [1.5s] ✔ | 15 19 | split_cols_by_groups ✔ | 9 11 | stat ✔ | 5 11 | summarize_ancova [1.9s] ✔ | 4 4 | summarize_change ✔ | 3 3 | summarize_colvars [1.2s] ✔ | 13 15 | summarize_coxreg [6.6s] ✔ | 17 25 | summarize_glm_count [3.3s] ✔ | 16 16 | summarize_num_patients [1.9s] ✔ | 9 9 | summarize_patients_exposure_in_cols [1.5s] ✔ | 7 10 | survival_biomarkers_subgroups [4.6s] ✔ | 8 9 | survival_coxph_pairwise [1.3s] ✔ | 14 17 | survival_duration_subgroups [9.8s] ✔ | 7 9 | survival_time ✔ | 10 11 | survival_timepoint [1.3s] ✔ | 11 12 | test_proportion_diff ✔ | 59 79 | utils [1.4s] ✔ | 42 | utils_checkmate ✔ | 6 24 | utils_default_stats_formats_labels ✔ | 13 26 | utils_factor ✔ | 3 3 | utils_ggplot [1.7s] ✔ | 8 | utils_grid ✔ | 19 24 | utils_rtables [7.6s] ✔ | 6 13 | utils_split_fun [2.9s] ══ Results ═════════════════════════════════════════════════════════════════════ Duration: 148.8 s ── Skipped tests (682) ───────────────────────────────────────────────────────── • On CRAN (682): 'test-abnormal.R:24:3', 'test-abnormal.R:51:3', 'test-abnormal.R:80:3', 'test-abnormal.R:103:3', 'test-abnormal.R:134:3', 'test-abnormal.R:160:3', 'test-abnormal.R:186:3', 'test-abnormal_by_baseline.R:16:3', 'test-abnormal_by_baseline.R:46:3', 'test-abnormal_by_baseline.R:80:3', 'test-abnormal_by_baseline.R:94:3', 'test-abnormal_by_baseline.R:113:3', 'test-abnormal_by_marked.R:80:3', 'test-abnormal_by_marked.R:135:3', 'test-abnormal_by_marked.R:254:3', 'test-abnormal_by_worst_grade.R:43:3', 'test-abnormal_by_worst_grade.R:71:3', 'test-abnormal_by_worst_grade_worsen.R:64:3', 'test-abnormal_by_worst_grade_worsen.R:82:3', 'test-abnormal_by_worst_grade_worsen.R:102:3', 'test-abnormal_by_worst_grade_worsen.R:122:3', 'test-abnormal_by_worst_grade_worsen.R:142:3', 'test-abnormal_by_worst_grade_worsen.R:162:3', 'test-abnormal_by_worst_grade_worsen.R:184:3', 'test-abnormal_by_worst_grade_worsen.R:211:3', 'test-analyze_variables.R:7:3', 'test-analyze_variables.R:17:3', 'test-analyze_variables.R:31:3', 'test-analyze_variables.R:39:3', 'test-analyze_variables.R:48:3', 'test-analyze_variables.R:62:3', 'test-analyze_variables.R:72:3', 'test-analyze_variables.R:81:3', 'test-analyze_variables.R:90:3', 'test-analyze_variables.R:113:3', 'test-analyze_variables.R:127:3', 'test-analyze_variables.R:134:3', 'test-analyze_variables.R:143:3', 'test-analyze_variables.R:152:3', 'test-analyze_variables.R:171:3', 'test-analyze_variables.R:228:3', 'test-analyze_variables.R:245:3', 'test-analyze_variables.R:275:3', 'test-analyze_variables.R:295:3', 'test-analyze_variables.R:310:3', 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'test-summarize_patients_exposure_in_cols.R:109:3', 'test-summarize_patients_exposure_in_cols.R:126:3', 'test-summarize_patients_exposure_in_cols.R:144:5', 'test-summarize_patients_exposure_in_cols.R:166:3', 'test-survival_biomarkers_subgroups.R:36:3', 'test-survival_biomarkers_subgroups.R:65:3', 'test-survival_biomarkers_subgroups.R:85:3', 'test-survival_biomarkers_subgroups.R:105:3', 'test-survival_biomarkers_subgroups.R:129:3', 'test-survival_biomarkers_subgroups.R:153:3', 'test-survival_biomarkers_subgroups.R:178:3', 'test-survival_coxph_pairwise.R:18:3', 'test-survival_coxph_pairwise.R:39:3', 'test-survival_coxph_pairwise.R:59:3', 'test-survival_coxph_pairwise.R:80:3', 'test-survival_coxph_pairwise.R:102:3', 'test-survival_coxph_pairwise.R:125:3', 'test-survival_coxph_pairwise.R:147:3', 'test-survival_coxph_pairwise.R:172:3', 'test-survival_duration_subgroups.R:33:3', 'test-survival_duration_subgroups.R:52:3', 'test-survival_duration_subgroups.R:68:3', 'test-survival_duration_subgroups.R:87:3', 'test-survival_duration_subgroups.R:102:3', 'test-survival_duration_subgroups.R:117:3', 'test-survival_duration_subgroups.R:134:3', 'test-survival_duration_subgroups.R:153:3', 'test-survival_duration_subgroups.R:175:3', 'test-survival_duration_subgroups.R:210:3', 'test-survival_duration_subgroups.R:229:3', 'test-survival_duration_subgroups.R:249:3', 'test-survival_duration_subgroups.R:267:3', 'test-survival_duration_subgroups.R:288:3', 'test-survival_time.R:16:3', 'test-survival_time.R:37:3', 'test-survival_time.R:56:3', 'test-survival_time.R:83:3', 'test-survival_time.R:106:3', 'test-survival_time.R:129:3', 'test-survival_time.R:154:3', 'test-survival_timepoint.R:17:3', 'test-survival_timepoint.R:39:3', 'test-survival_timepoint.R:61:3', 'test-survival_timepoint.R:86:3', 'test-survival_timepoint.R:112:3', 'test-survival_timepoint.R:137:3', 'test-survival_timepoint.R:162:3', 'test-survival_timepoint.R:188:3', 'test-survival_timepoint.R:215:3', 'test-survival_timepoint.R:238:3', 'test-test_proportion_diff.R:12:3', 'test-test_proportion_diff.R:24:3', 'test-test_proportion_diff.R:49:3', 'test-test_proportion_diff.R:63:3', 'test-test_proportion_diff.R:89:3', 'test-test_proportion_diff.R:113:3', 'test-test_proportion_diff.R:133:3', 'test-test_proportion_diff.R:151:3', 'test-test_proportion_diff.R:169:3', 'test-test_proportion_diff.R:189:3', 'test-test_proportion_diff.R:211:3', 'test-utils.R:5:3', 'test-utils.R:17:3', 'test-utils.R:30:3', 'test-utils.R:37:3', 'test-utils.R:57:3', 'test-utils.R:65:3', 'test-utils.R:73:3', 'test-utils.R:96:3', 'test-utils.R:105:3', 'test-utils.R:113:3', 'test-utils.R:121:3', 'test-utils.R:167:3', 'test-utils.R:182:3', 'test-utils.R:191:3', 'test-utils.R:202:5', 'test-utils.R:212:3', 'test-utils.R:223:3', 'test-utils.R:234:5', 'test-utils.R:246:5', 'test-utils.R:256:3', 'test-utils.R:267:3', 'test-utils.R:278:5', 'test-utils.R:290:5', 'test-utils.R:300:3', 'test-utils.R:311:3', 'test-utils.R:322:5', 'test-utils.R:334:5', 'test-utils.R:344:3', 'test-utils.R:355:3', 'test-utils.R:366:5', 'test-utils.R:378:5', 'test-utils.R:388:3', 'test-utils.R:399:3', 'test-utils.R:410:5', 'test-utils.R:422:5', 'test-utils.R:434:5', 'test-utils.R:446:3', 'test-utils.R:457:5', 'test-utils.R:469:5', 'test-utils.R:481:5', 'test-utils.R:504:5', 'test-utils.R:516:5', 'test-utils.R:528:5', 'test-utils.R:540:3', 'test-utils.R:551:5', 'test-utils.R:563:5', 'test-utils.R:573:3', 'test-utils.R:584:3', 'test-utils.R:595:5', 'test-utils.R:607:5', 'test-utils.R:619:5', 'test-utils.R:631:3', 'test-utils.R:642:5', 'test-utils.R:654:5', 'test-utils.R:666:5', 'test-utils.R:678:3', 'test-utils.R:689:5', 'test-utils.R:701:5', 'test-utils.R:713:5', 'test-utils_default_stats_formats_labels.R:4:3', 'test-utils_default_stats_formats_labels.R:108:3', 'test-utils_default_stats_formats_labels.R:159:3', 'test-utils_default_stats_formats_labels.R:194:3', 'test-utils_default_stats_formats_labels.R:207:3', 'test-utils_default_stats_formats_labels.R:217:3', 'test-utils_factor.R:25:3', 'test-utils_factor.R:36:3', 'test-utils_factor.R:51:3', 'test-utils_factor.R:58:3', 'test-utils_factor.R:74:3', 'test-utils_factor.R:86:3', 'test-utils_factor.R:97:3', 'test-utils_factor.R:115:3', 'test-utils_factor.R:138:3', 'test-utils_factor.R:149:3', 'test-utils_factor.R:159:3', 'test-utils_factor.R:167:3', 'test-utils_factor.R:175:3', 'test-utils_ggplot.R:19:3', 'test-utils_ggplot.R:53:3', 'test-utils_ggplot.R:59:3', 'test-utils_rtables.R:14:3', 'test-utils_rtables.R:44:3', 'test-utils_rtables.R:52:3', 'test-utils_rtables.R:66:3', 'test-utils_rtables.R:71:3', 'test-utils_rtables.R:87:3', 'test-utils_rtables.R:94:3', 'test-utils_rtables.R:104:3', 'test-utils_rtables.R:119:3', 'test-utils_rtables.R:132:3', 'test-utils_rtables.R:159:3', 'test-utils_rtables.R:169:3', 'test-utils_rtables.R:181:3', 'test-utils_rtables.R:202:3', 'test-utils_rtables.R:215:3', 'test-utils_rtables.R:228:3', 'test-utils_rtables.R:235:3', 'test-utils_rtables.R:249:3', 'test-utils_rtables.R:261:3', 'test-utils_split_fun.R:27:3', 'test-utils_split_fun.R:39:3', 'test-utils_split_fun.R:56:3', 'test-utils_split_fun.R:74:3', 'test-utils_split_fun.R:102:3', 'test-utils_split_fun.R:124:3' ── Failed tests ──────────────────────────────────────────────────────────────── Failure ('test-coxreg.R:563:3'): fit_coxreg_multivar works correctly also without treatment arm stats::formula(result$mod) (`actual`) not equal to survival::Surv(time, status) ~ 1 + covar1 + covar2 (`expected`). `actual`: `Surv(time, status) ~ 1 + covar1 + covar2` `expected`: `survival::Surv(time, status) ~ 1 + covar1 + covar2` [ FAIL 1 | WARN 0 | SKIP 682 | PASS 1153 ] Deleting unused snapshots: • estimate_incidence_rate.md • g_forest/g_forest_custom_2.new.svg • g_forest/g_forest_custom_2.svg • g_forest/g_forest_custom_3.new.svg • g_forest/g_forest_custom_3.svg • g_forest/g_forest_or.new.svg • g_forest/g_forest_or.svg • g_forest/g_forest_plot_only.new.svg • g_forest/g_forest_plot_only.svg • g_km/g_km_crop_ylim_failure.new.svg • g_km/g_km_crop_ylim_failure.svg • g_km/g_km_custom_ylim.new.svg • g_km/g_km_custom_ylim.svg • g_km/g_km_table_only.new.svg • g_km/g_km_table_only.svg • g_lineplot/g_lineplot_xlim_ylim.svg • g_lineplot/g_lineplot_xticks.svg • utils_ggplot/df2gg_cw.svg • utils_ggplot/df2gg_fs.svg • utils_ggplot/rtable2gg_cw.svg • utils_ggplot/rtable2gg_fs.svg • utils_ggplot/rtable2gg_lblpad.svg Error: Test failures Execution halted Package: tram Check: tests New result: NOTE Running ‘Coxph-Ex.R’ [8s/8s] Comparing ‘Coxph-Ex.Rout’ to ‘Coxph-Ex.Rout.save’ ... OK Running ‘KaplanMeier-Ex.R’ [2s/2s] Running ‘PI_OVL-Ex.R’ [3s/3s] Running ‘Polr-Ex.R’ [2s/2s] Comparing ‘Polr-Ex.Rout’ to ‘Polr-Ex.Rout.save’ ... OK Running ‘Survreg-Ex.R’ [3s/3s] Comparing ‘Survreg-Ex.Rout’ to ‘Survreg-Ex.Rout.save’ ...24,25c24 < survreg(formula = Surv(time, cens) ~ pnodes + age, data = GBSG2, < dist = "weibull") --- > survreg(formula = fm, data = GBSG2, dist = "weibull") 76,77c75 < survreg(formula = Surv(time, cens) ~ pnodes + age + strata(horTh), < data = GBSG2, dist = "weibull") --- > survreg(formula = fms, data = GBSG2, dist = "weibull") 131,132c129 < survreg(formula = Surv(time, cens) ~ pnodes + age, data = GBSG2, < dist = "exponential") --- > survreg(formula = fm, data = GBSG2, dist = "exponential") 225,226c222 < survreg(formula = Surv(time, cens) ~ pnodes + age, data = GBSG2, < dist = "rayleigh") --- > survreg(formula = fm, data = GBSG2, dist = "rayleigh") 319,320c315 < survreg(formula = Surv(time, cens) ~ pnodes + age, data = GBSG2, < dist = "lognormal") --- > survreg(formula = fm, data = GBSG2, dist = "lognormal") 368,369c363 < survreg(formula = Surv(time, cens) ~ pnodes + age + strata(horTh), < data = GBSG2, dist = "lognormal") --- > survreg(formula = fms, data = GBSG2, dist = "lognormal") 423,424c417 < survreg(formula = Surv(time, cens) ~ pnodes + age, data = GBSG2, < dist = "loglogistic") --- > survreg(formula = fm, data = GBSG2, dist = "loglogistic") 475,476c468 < survreg(formula = Surv(time, cens) ~ pnodes + age + strata(horTh), < data = GBSG2, dist = "loglogistic") --- > survreg(formula = fms, data = GBSG2, dist = "loglogistic") Running ‘bugfixes.R’ [47s/47s] Running ‘intercepts-Ex.R’ [3s/3s] Comparing ‘intercepts-Ex.Rout’ to ‘intercepts-Ex.Rout.save’ ... OK Running ‘mmlt-Ex.R’ [99s/99s] Comparing ‘mmlt-Ex.Rout’ to ‘mmlt-Ex.Rout.save’ ... OK Running ‘mmlt-interface.R’ [30s/30s] Running ‘mtram-Ex.R’ [12s/12s] Comparing ‘mtram-Ex.Rout’ to ‘mtram-Ex.Rout.save’ ... OK Running ‘stram-Ex.R’ [9s/9s] Comparing ‘stram-Ex.Rout’ to ‘stram-Ex.Rout.save’ ... OK