Package check result: NOTE Check: CRAN incoming feasibility, Result: NOTE Maintainer: ‘Terry M Therneau ’ Size of tarball: 9415389 bytes Changes to worse in reverse depends: 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: 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: netdiffuseR Check: examples New result: ERROR Running examples in ‘netdiffuseR-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: exposure > ### Title: Ego exposure > ### Aliases: exposure > ### Keywords: univar > > ### ** Examples > > # Calculating lagged exposure ----------------------------------------------- > > set.seed(8) > graph <- rdiffnet(20, 4) Warning in (function (graph, p, algorithm = "endpoints", both.ends = FALSE, : The option -copy.first- is set to TRUE. In this case, the first graph will be treated as a baseline, and thus, networks after T=1 will be replaced with T-1. > > expo0 <- exposure(graph) > expo1 <- exposure(graph, lags = 1) > > # These should be equivalent > stopifnot(all(expo0[, -4] == expo1[, -1])) # No stop! > > > # Calculating the exposure based on Structural Equivalence ------------------ > set.seed(113132) > graph <- rdiffnet(100, 4) Warning in (function (graph, p, algorithm = "endpoints", both.ends = FALSE, : The option -copy.first- is set to TRUE. In this case, the first graph will be treated as a baseline, and thus, networks after T=1 will be replaced with T-1. > > SE <- lapply(struct_equiv(graph), "[[", "SE") > SE <- lapply(SE, function(x) { + x <- 1/x + x[!is.finite(x)] <- 0 + x + }) > > > # These three lines are equivalent to: > expo_se2 <- exposure(graph, alt.graph="se", valued=TRUE) > # Notice that we are setting valued=TRUE, but this is not necesary since when > # alt.graph = "se" the function checks this to be setted equal to TRUE > > # Weighted Exposure using degree -------------------------------------------- > eDE <- exposure(graph, attrs=dgr(graph)) > > # Which is equivalent to > graph[["deg"]] <- dgr(graph) > eDE2 <- exposure(graph, attrs="deg") > > # Comparing using incoming edges ------------------------------------------- > eIN <- exposure(graph, outgoing=FALSE) > > # Structral equivalence for different communities --------------------------- > data(medInnovationsDiffNet) > > # Only using 4 time slides, this is for convenience > medInnovationsDiffNet <- medInnovationsDiffNet[, , 1:4] > > # METHOD 1: Using the c.diffnet method: > > # Creating subsets by city > cities <- unique(medInnovationsDiffNet[["city"]]) > > diffnet <- medInnovationsDiffNet[medInnovationsDiffNet[["city"]] == cities[1]] > diffnet[["expo_se"]] <- exposure(diffnet, alt.graph="se", valued=TRUE) *** caught segfault *** address (nil), cause 'unknown' Traceback: 1: .M2C(newTMat(i = c(ij1[, 1], ij2[, 1]), j = c(ij1[, 2], ij2[, 2]), x = if (Generic == "+") c(e1@x, e2@x) else c(e1@x, -e2@x))) 2: .Arith.Csparse(e1, e2, .Generic, class. = "dgCMatrix") 3: d[, ids[, 1]] - d[, ids[, 2]] 4: d[, ids[, 1]] - d[, ids[, 2]] 5: euclidean_distance(gdist) 6: struct_equiv_new(geod, v) 7: struct_equiv.dgCMatrix(methods::as(graph[[i]], "dgCMatrix"), v, inf.replace, groupvar, ...) 8: struct_equiv.list(graph, v, inf.replace, groupvar, ...) 9: struct_equiv(graph, groupvar = groupvar, ...) 10: lapply(struct_equiv(graph, groupvar = groupvar, ...), "[[", "SE") 11: exposure(diffnet, alt.graph = "se", valued = TRUE) An irrecoverable exception occurred. R is aborting now ... Segmentation fault 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