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Type 'q()' to quit R. > require(robustbase) Loading required package: robustbase > > set.seed(17)# reproducibility! > ## to check: > ## - for the empty model > summary(lmrob(Y ~ 0, coleman)) Call: lmrob(formula = Y ~ 0, data = coleman) \--> method = "" Residuals: Min 1Q Median 3Q Max 22.70 32.78 35.86 39.95 43.10 No Coefficients > ## - with and without an intercept in the model > summary(lmrob(Y ~ 1, coleman)) Call: lmrob(formula = Y ~ 1, data = coleman) \--> method = "MM" Residuals: Min 1Q Median 3Q Max -12.8605 -2.7855 0.2945 4.3895 7.5395 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 35.560 1.342 26.5 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Robust residual standard error: 5.48 Convergence in 9 IRWLS iterations Robustness weights: one weight is ~= 1. The remaining 19 ones are summarized as Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5611 0.8986 0.9553 0.9044 0.9918 0.9987 Algorithmic parameters: tuning.chi bb tuning.psi refine.tol 1.548e+00 5.000e-01 4.685e+00 1.000e-07 rel.tol scale.tol solve.tol zero.tol 1.000e-07 1.000e-10 1.000e-07 1.000e-10 eps.outlier eps.x warn.limit.reject warn.limit.meanrw 5.000e-03 1.819e-12 5.000e-01 5.000e-01 nResample max.it best.r.s k.fast.s k.max 500 50 2 1 200 maxit.scale trace.lev mts compute.rd fast.s.large.n 200 0 1000 0 2000 psi subsampling cov "bisquare" "nonsingular" ".vcov.avar1" compute.outlier.stats "SM" seed : int(0) > writeLines(sfm <- capture.output( + summary(lmrob(Y ~ ., coleman)))) # and this must be "identical": Call: lmrob(formula = Y ~ ., data = coleman) \--> method = "MM" Residuals: Min 1Q Median 3Q Max -4.16181 -0.39226 0.01611 0.55619 7.22766 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 30.50232 6.71260 4.544 0.000459 *** salaryP -1.66615 0.43129 -3.863 0.001722 ** fatherWc 0.08425 0.01467 5.741 5.10e-05 *** sstatus 0.66774 0.03385 19.726 1.30e-11 *** teacherSc 1.16778 0.10983 10.632 4.35e-08 *** motherLev -4.13657 0.92084 -4.492 0.000507 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Robust residual standard error: 1.134 Multiple R-squared: 0.9814, Adjusted R-squared: 0.9747 Convergence in 11 IRWLS iterations Robustness weights: observation 18 is an outlier with |weight| = 0 ( < 0.005); The remaining 19 ones are summarized as Min. 1st Qu. Median Mean 3rd Qu. Max. 0.1491 0.9412 0.9847 0.9279 0.9947 0.9982 Algorithmic parameters: tuning.chi bb tuning.psi refine.tol 1.548e+00 5.000e-01 4.685e+00 1.000e-07 rel.tol scale.tol solve.tol zero.tol 1.000e-07 1.000e-10 1.000e-07 1.000e-10 eps.outlier eps.x warn.limit.reject warn.limit.meanrw 5.000e-03 1.569e-10 5.000e-01 5.000e-01 nResample max.it best.r.s k.fast.s k.max 500 50 2 1 200 maxit.scale trace.lev mts compute.rd fast.s.large.n 200 0 1000 0 2000 psi subsampling cov "bisquare" "nonsingular" ".vcov.avar1" compute.outlier.stats "SM" seed : int(0) > sfm2 <- capture.output(summary(lmrob(Y ~ ., coleman, model=FALSE, x=FALSE, y=FALSE))) > iCall <- grep("lmrob.*coleman", sfm)# the only line that differs > stopifnot(sfm[-iCall] == sfm2[-iCall]) > ## w/o intercept: > summary(lmrob(Y ~ . - 1, coleman, model=FALSE, x=FALSE, y=FALSE)) Call: lmrob(formula = Y ~ . - 1, data = coleman, model = FALSE, x = FALSE, y = FALSE) \--> method = "MM" Residuals: Min 1Q Median 3Q Max -4.86146 -0.59195 -0.04679 0.87826 5.40639 Coefficients: Estimate Std. Error t value Pr(>|t|) salaryP -1.97540 0.45262 -4.364 0.000555 *** fatherWc 0.03388 0.02220 1.526 0.147749 sstatus 0.55922 0.07590 7.367 2.34e-06 *** teacherSc 1.60446 0.19039 8.427 4.51e-07 *** motherLev -0.48903 0.90805 -0.539 0.598097 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Robust residual standard error: 1.344 Multiple R-squared: 0.9987, Adjusted R-squared: 0.9983 Convergence in 14 IRWLS iterations Robustness weights: 3 weights are ~= 1. The remaining 17 ones are summarized as Min. 1st Qu. Median Mean 3rd Qu. Max. 0.06899 0.89030 0.95860 0.82750 0.98700 0.99820 Algorithmic parameters: tuning.chi bb tuning.psi refine.tol 1.548e+00 5.000e-01 4.685e+00 1.000e-07 rel.tol scale.tol solve.tol zero.tol 1.000e-07 1.000e-10 1.000e-07 1.000e-10 eps.outlier eps.x warn.limit.reject warn.limit.meanrw 5.000e-03 1.569e-10 5.000e-01 5.000e-01 nResample max.it best.r.s k.fast.s k.max 500 50 2 1 200 maxit.scale trace.lev mts compute.rd fast.s.large.n 200 0 1000 0 2000 psi subsampling cov "bisquare" "nonsingular" ".vcov.avar1" compute.outlier.stats "SM" seed : int(0) > > ## - when prior-weights are included > wts <- c(rep(0.05, 10), rep(2, 10)) > summary(lmrob(Y ~ . - 1, coleman, model=FALSE, x=FALSE, y=FALSE, + weights = wts)) Call: lmrob(formula = Y ~ . - 1, data = coleman, weights = wts, model = FALSE, x = FALSE, y = FALSE) \--> method = "MM" Weighted Residuals: Min 1Q Median 3Q Max -1.01509 -0.04288 0.04892 0.38289 9.00119 Coefficients: Estimate Std. Error t value Pr(>|t|) salaryP -2.68644 0.05871 -45.761 < 2e-16 *** fatherWc 0.04761 0.00721 6.603 8.39e-06 *** sstatus 0.58362 0.00314 185.842 < 2e-16 *** teacherSc 1.77115 0.07918 22.369 6.20e-13 *** motherLev -1.03171 0.34154 -3.021 0.0086 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Robust residual standard error: 0.423 Multiple R-squared: 0.9985, Adjusted R-squared: 0.998 Convergence in 5 IRWLS iterations Robustness weights: 3 observations c(12,16,18) are outliers with |weight| = 0 ( < 0.005); 5 weights are ~= 1. The remaining 12 ones are summarized as Min. 1st Qu. Median Mean 3rd Qu. Max. 0.5441 0.9257 0.9833 0.9303 0.9956 0.9985 Algorithmic parameters: tuning.chi bb tuning.psi refine.tol 1.548e+00 5.000e-01 4.685e+00 1.000e-07 rel.tol scale.tol solve.tol zero.tol 1.000e-07 1.000e-10 1.000e-07 1.000e-10 eps.outlier eps.x warn.limit.reject warn.limit.meanrw 5.000e-03 2.219e-10 5.000e-01 5.000e-01 nResample max.it best.r.s k.fast.s k.max 500 50 2 1 200 maxit.scale trace.lev mts compute.rd fast.s.large.n 200 0 1000 0 2000 psi subsampling cov "bisquare" "nonsingular" ".vcov.avar1" compute.outlier.stats "SM" seed : int(0) > ## - should work for object with NA in the coefficients, and > ## - should work for object with NA in the observations --> both in ./NAcoef.R > > ## check equality with lm() for classical model > test <- function(formula, data, + items=c("coefficients", "residuals", "df", "scale", + "r.squared", "adj.r.squared", "weights"), + tol = 1e-4, ...) + { + lmrCtrl <- lmrob.control(psi = "hampel", tuning.psi = c(1000, 2000, 3000), + method="SMDM", ...) + sc <- summary(lm (formula, data)) + sr <- summary(lmrob(formula, data, control= lmrCtrl)) + names(sc)[names(sc) == "sigma"] <- "scale" + if(sc$df[1] == 0 && getRversion() <= "3.5.1" && as.numeric(R.version$`svn rev`) < 74993) + ## in the past, lm() returned logical empty matrix + storage.mode(sc$coefficients) <- "double" + ret <- all.equal(sc[items], sr[items], tolerance=tol) + if (!isTRUE(ret)) { + print(sr) + for (i in seq_along(items)) { + print(sc[items[i]]) + print(sr[items[i]]) + } + print(ret) + stop(sprintf("all.equal(sc[items], sr[items], tol.. = %g) are not all TRUE", + tol)) + } + ret + } > > set.seed(101) > > test(Y ~ 0, coleman, c("residuals", "df", "coefficients", + "r.squared", "adj.r.squared"), tol=1e-10) [1] TRUE > test(Y ~ 1, coleman, tol = 2e-4) [1] TRUE > test(Y ~ ., coleman, tol = 4e-4) [1] TRUE > test(Y ~ . - 1, coleman, tol = 4e-4) [1] TRUE > > > proc.time() user system elapsed 0.60 0.06 0.65