Last updated on 2026-01-30 05:51:03 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.99-6 | 27.46 | 274.07 | 301.53 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.99-6 | 16.21 | 179.15 | 195.36 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.99-6 | 44.00 | 460.05 | 504.05 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 0.99-6 | 43.00 | 458.53 | 501.53 | ERROR | |
| r-devel-windows-x86_64 | 0.99-6 | 37.00 | 351.00 | 388.00 | OK | |
| r-patched-linux-x86_64 | 0.99-6 | 28.13 | 254.78 | 282.91 | OK | |
| r-release-linux-x86_64 | 0.99-6 | 23.26 | 253.42 | 276.68 | OK | |
| r-release-macos-arm64 | 0.99-6 | OK | ||||
| r-release-macos-x86_64 | 0.99-6 | 19.00 | 263.00 | 282.00 | OK | |
| r-release-windows-x86_64 | 0.99-6 | 36.00 | 333.00 | 369.00 | OK | |
| r-oldrel-macos-arm64 | 0.99-6 | OK | ||||
| r-oldrel-macos-x86_64 | 0.99-6 | 18.00 | 261.00 | 279.00 | OK | |
| r-oldrel-windows-x86_64 | 0.99-6 | 38.00 | 419.00 | 457.00 | OK |
Version: 0.99-6
Check: tests
Result: ERROR
Running ‘LTS-specials.R’ [0s/1s]
Running ‘MCD-specials.R’ [0s/0s]
Comparing ‘MCD-specials.Rout’ to ‘MCD-specials.Rout.save’ ... OK
Running ‘MT-tst.R’ [4s/5s]
Running ‘NAcoef.R’ [0s/1s]
Running ‘OGK-ex.R’ [0s/1s]
Comparing ‘OGK-ex.Rout’ to ‘OGK-ex.Rout.save’ ... OK
Running ‘Qn-Sn-plots.R’ [1s/1s]
Running ‘Rsquared.R’ [1s/1s]
Comparing ‘Rsquared.Rout’ to ‘Rsquared.Rout.save’ ... OK
Running ‘binom-ni-small.R’ [0s/1s]
Comparing ‘binom-ni-small.Rout’ to ‘binom-ni-small.Rout.save’ ... OK
Running ‘binom-no-x.R’ [0s/0s]
Running ‘comedian-tst.R’ [0s/1s]
Running ‘exact-fit-categorical.R’ [0s/1s]
Running ‘glmrob-1.R’ [5s/6s]
Running ‘glmrob-specials.R’ [0s/1s]
Running ‘huber-etc.R’ [0s/1s]
Comparing ‘huber-etc.Rout’ to ‘huber-etc.Rout.save’ ... OK
Running ‘large-values.R’ [0s/1s]
Running ‘lmrob-data.R’ [2s/2s]
Running ‘lmrob-ex12.R’ [2s/3s]
Running ‘lmrob-methods.R’ [0s/1s]
Comparing ‘lmrob-methods.Rout’ to ‘lmrob-methods.Rout.save’ ... OK
Running ‘lmrob-psifns.R’ [3s/4s]
Comparing ‘lmrob-psifns.Rout’ to ‘lmrob-psifns.Rout.save’ ... OK
Running ‘m-s-estimator.R’ [1s/1s]
Running ‘mc-etc.R’ [1s/1s]
Running ‘mc-strict.R’ [9s/10s]
Running ‘nlregrob-tst.R’ [8s/9s]
Running ‘nlrob-tst.R’ [2s/2s]
Running ‘poisson-ex.R’ [1s/1s]
Running ‘psi-rho-etc.R’ [1s/1s]
Comparing ‘psi-rho-etc.Rout’ to ‘psi-rho-etc.Rout.save’ ... OK
Running ‘small-sample.R’ [5s/5s]
Comparing ‘small-sample.Rout’ to ‘small-sample.Rout.save’ ... OK
Running ‘subsample.R’ [5s/6s]
Running ‘tlts.R’ [1s/1s]
Comparing ‘tlts.Rout’ to ‘tlts.Rout.save’ ... OK
Running ‘tmcd.R’ [4s/5s]
Running ‘weights.R’ [1s/1s]
Comparing ‘weights.Rout’ to ‘weights.Rout.save’ ... OK
Running ‘wgt-himed-xtra.R’ [2s/2s]
Running ‘wgt-himed.R’ [0s/0s]
Comparing ‘wgt-himed.Rout’ to ‘wgt-himed.Rout.save’ ... OK
Running the tests in ‘tests/NAcoef.R’ failed.
Complete output:
> ## test handing of NA coefficients / singular fits
> ## also check:
> ## -- what would have to be done if class "lm" was added.
> ## -- general compatibility to class lm.
> require(robustbase)
Loading required package: robustbase
> options(digits = 5)# -> higher chance of platform independence
>
> ## generate simple example data (almost as in ./weights.R )
> data <- expand.grid(x1=letters[1:3], x2=LETTERS[1:3], rep=1:3)
> set.seed(1)
> data$y <- rnorm(nrow(data))
> ## drop all combinations of one interaction:
> data <- subset(data, x1 != 'c' | (x2 != 'B' & x2 != 'C'))
> ## add collinear variables
> data$x3 <- rnorm(nrow(data))
> data$x4 <- rnorm(nrow(data))
> data$x5 <- data$x3 + data$x4
> ## add some NA terms
> data$y[1] <- NA
> data$x4[2:3] <- NA ## to test anova
>
> ## Classical models start with 'cm', robust just with 'rm' (or just 'm'):
> cm0 <- lm (y ~ x1*x2 + x3, data)
> cm1 <- lm (y ~ x1*x2 + x3 + x4 + x5, data)
> set.seed(2)
> rm1 <- lmrob(y ~ x1*x2 + x3 + x4 + x5, data)
> m3 <- lmrob(y ~ x1*x2 + x3 + x4, data) # same column space as rm1
> rm0 <- lmrob(y ~ x1*x2 + x3, data)
>
> ## clean version of rm1 (to check predict)
> data2 <- data.frame(y=data$y[-(1:3)], rm1$x[,!is.na(rm1$coef)])
> set.seed(2)
> rm1c <- lmrob(y ~ x1b + x1c + x2B + x2C + x3 + x4 + x1b:x2B + x1b:x2C, data2)
>
> ## add class lm to rm1 (for now)
> class(rm1) <- c(class(rm1), "lm")
> class(rm0) <- c(class(rm0), "lm")
>
> ## the full matrix (data) should be returned by model matrix (frame)
> stopifnot(all.equal(model.matrix(cm1), model.matrix(rm1)),
+ all.equal(model.frame (cm1), model.frame (rm1)))
> ## qr decomposition should be for the full data and pivots identical lm result
> qr.cm1 <- qr(cm1)$qr
> qr.rm1 <- rm1$qr$qr
> stopifnot(NCOL(qr.rm1) == NCOL(qr.cm1),
+ NROW(qr.rm1) == NROW(qr.cm1),
+ length(rm1$qr$qraux) == length(qr(cm1)$qraux),
+ all.equal(rm1$qr$pivot, qr(cm1)$pivot),
+ all.equal(dimnames(qr.rm1),dimnames(qr.cm1)))
> ## the alias function should return the same result
> stopifnot(all.equal(alias(cm1), alias(rm1)))
>
> ####
> ## these helper functions should print NAs for the dropped coefficients
> print(rm1)
Call:
lmrob(formula = y ~ x1 * x2 + x3 + x4 + x5, data = data)
\--> method = "MM"
Coefficients:
(Intercept) x1b x1c x2B x2C x3
0.4381 0.5968 0.0344 0.2012 0.1789 -0.1320
x4 x5 x1b:x2B x1c:x2B x1b:x2C x1c:x2C
-0.2155 NA -1.8763 NA -0.8651 NA
> summary(rm1) -> s1
> confint(rm1) -> ci1
> stopifnot(identical(is.na(coef(cm1)), apply(ci1, 1L, anyNA)),
+ identical(sigma(rm1), s1$ sigma),
+ identical(vcov(rm1, complete=FALSE), s1$ cov ),
+ TRUE)
>
> print(s1, showAlgo=FALSE)
Call:
lmrob(formula = y ~ x1 * x2 + x3 + x4 + x5, data = data)
\--> method = "MM"
Residuals:
Min 1Q Median 3Q Max
-1.4584 -0.3556 0.0246 0.3651 1.0296
Coefficients: (3 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4381 0.5443 0.80 0.44
x1b 0.5968 0.6423 0.93 0.38
x1c 0.0344 0.6880 0.05 0.96
x2B 0.2012 0.7164 0.28 0.79
x2C 0.1789 0.6871 0.26 0.80
x3 -0.1320 0.4155 -0.32 0.76
x4 -0.2155 0.1694 -1.27 0.24
x5 NA NA NA NA
x1b:x2B -1.8763 1.2153 -1.54 0.16
x1c:x2B NA NA NA NA
x1b:x2C -0.8651 0.7466 -1.16 0.28
x1c:x2C NA NA NA NA
Robust residual standard error: 0.927
(3 observations deleted due to missingness)
Multiple R-squared: 0.338, Adjusted R-squared: -0.251
Convergence in 15 IRWLS iterations
Robustness weights:
2 weights are ~= 1. The remaining 16 ones are summarized as
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.787 0.937 0.985 0.952 0.988 0.994
> ci1
2.5 % 97.5 %
(Intercept) -0.79333 1.66946
x1b -0.85607 2.04973
x1c -1.52188 1.59076
x2B -1.41948 1.82189
x2C -1.37549 1.73320
x3 -1.07182 0.80783
x4 -0.59863 0.16756
x5 NA NA
x1b:x2B -4.62539 0.87283
x1c:x2B NA NA
x1b:x2C -2.55391 0.82381
x1c:x2C NA NA
> ## drop1 should return df = 0
> #drop1(rm1) ## drop.lm does not return valid results (yet)!
>
> ####
> ## methods that should just drop the NA coefficients
> ## m3 is actually the same as rm1, so anova should raise an error
> tools::assertError(anova(rm1, m3, test="Wald"))
> tools::assertError(anova(rm1, m3, test="Deviance"))
> ## but comparing rm1 and rm0 should be ok
> anova(rm1, rm0, test="Wald")
Robust Wald Test Table
Model 1: y ~ x1 * x2 + x3 + x4 + x5
Model 2: y ~ x1 * x2 + x3
Largest model fitted by lmrob(), i.e. SM
pseudoDf Test.Stat Df Pr(>chisq)
1 6
2 10 1.62 1 0.2
> anova(rm1, rm0, test="Deviance")
Robust Deviance Table
Model 1: y ~ x1 * x2 + x3 + x4 + x5
Model 2: y ~ x1 * x2 + x3
Largest model fitted by lmrob(), i.e. SM
pseudoDf Test.Stat Df Pr(>chisq)
1 6
2 10 1.4 1 0.24
> ## commands with single #:
> ## they do (or might) not return sensible results for robust fits
> ## and need to be checked again
> #cooks.distance(rm1)
> #deviance(rm1)
> #dfbeta(rm1)
> #dfbetas(rm1)
> #effects(rm1) ## fails
> #extractAIC(rm1)
> #influence(rm1)
> stopifnot(all.equal(hv1 <- hatvalues(rm1), .lmrob.hat(wqr=rm1$qr), tol=1e-15),
+ all.equal(hv1, stats:::hatvalues.lm(rm1), tol=1e-15),
+ all.equal(hat(cm1$qr), unname(hatvalues(cm1)), tol=1e-15),
+ all.equal(unname(hv1), hat(rm1$qr), tol=1e-15),
+ ## ditto :
+ all.equal(hv1c <- hatvalues(rm1c), stats:::hatvalues.lm(rm1c), tol=1e-15))
Error in match.arg(type) : 'arg' should be one of "prior", "robustness"
Calls: stopifnot ... weighted.residuals -> weights -> weights.lmrob -> match.arg
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.99-6
Check: tests
Result: ERROR
Running ‘LTS-specials.R’
Running ‘MCD-specials.R’
Comparing ‘MCD-specials.Rout’ to ‘MCD-specials.Rout.save’ ... OK
Running ‘MT-tst.R’ [12s/26s]
Running ‘NAcoef.R’
Running ‘OGK-ex.R’
Comparing ‘OGK-ex.Rout’ to ‘OGK-ex.Rout.save’ ... OK
Running ‘Qn-Sn-plots.R’
Running ‘Rsquared.R’
Comparing ‘Rsquared.Rout’ to ‘Rsquared.Rout.save’ ... OK
Running ‘binom-ni-small.R’
Comparing ‘binom-ni-small.Rout’ to ‘binom-ni-small.Rout.save’ ... OK
Running ‘binom-no-x.R’
Running ‘comedian-tst.R’
Running ‘exact-fit-categorical.R’
Running ‘glmrob-1.R’ [14s/33s]
Running ‘glmrob-specials.R’
Running ‘huber-etc.R’
Comparing ‘huber-etc.Rout’ to ‘huber-etc.Rout.save’ ... OK
Running ‘large-values.R’
Running ‘lmrob-data.R’ [5s/14s]
Running ‘lmrob-ex12.R’ [4s/11s]
Running ‘lmrob-methods.R’
Comparing ‘lmrob-methods.Rout’ to ‘lmrob-methods.Rout.save’ ... OK
Running ‘lmrob-psifns.R’ [7s/17s]
Comparing ‘lmrob-psifns.Rout’ to ‘lmrob-psifns.Rout.save’ ... OK
Running ‘m-s-estimator.R’
Running ‘mc-etc.R’
Running ‘mc-strict.R’ [26s/67s]
Running ‘nlregrob-tst.R’ [29s/81s]
Running ‘nlrob-tst.R’ [4s/14s]
Running ‘poisson-ex.R’
Running ‘psi-rho-etc.R’
Comparing ‘psi-rho-etc.Rout’ to ‘psi-rho-etc.Rout.save’ ... OK
Running ‘small-sample.R’ [15s/35s]
Comparing ‘small-sample.Rout’ to ‘small-sample.Rout.save’ ... OK
Running ‘subsample.R’ [10s/24s]
Running ‘tlts.R’
Comparing ‘tlts.Rout’ to ‘tlts.Rout.save’ ... OK
Running ‘tmcd.R’ [11s/33s]
Running ‘weights.R’
Comparing ‘weights.Rout’ to ‘weights.Rout.save’ ... OK
Running ‘wgt-himed-xtra.R’ [4s/12s]
Running ‘wgt-himed.R’
Comparing ‘wgt-himed.Rout’ to ‘wgt-himed.Rout.save’ ... OK
Running the tests in ‘tests/NAcoef.R’ failed.
Complete output:
> ## test handing of NA coefficients / singular fits
> ## also check:
> ## -- what would have to be done if class "lm" was added.
> ## -- general compatibility to class lm.
> require(robustbase)
Loading required package: robustbase
> options(digits = 5)# -> higher chance of platform independence
>
> ## generate simple example data (almost as in ./weights.R )
> data <- expand.grid(x1=letters[1:3], x2=LETTERS[1:3], rep=1:3)
> set.seed(1)
> data$y <- rnorm(nrow(data))
> ## drop all combinations of one interaction:
> data <- subset(data, x1 != 'c' | (x2 != 'B' & x2 != 'C'))
> ## add collinear variables
> data$x3 <- rnorm(nrow(data))
> data$x4 <- rnorm(nrow(data))
> data$x5 <- data$x3 + data$x4
> ## add some NA terms
> data$y[1] <- NA
> data$x4[2:3] <- NA ## to test anova
>
> ## Classical models start with 'cm', robust just with 'rm' (or just 'm'):
> cm0 <- lm (y ~ x1*x2 + x3, data)
> cm1 <- lm (y ~ x1*x2 + x3 + x4 + x5, data)
> set.seed(2)
> rm1 <- lmrob(y ~ x1*x2 + x3 + x4 + x5, data)
> m3 <- lmrob(y ~ x1*x2 + x3 + x4, data) # same column space as rm1
> rm0 <- lmrob(y ~ x1*x2 + x3, data)
>
> ## clean version of rm1 (to check predict)
> data2 <- data.frame(y=data$y[-(1:3)], rm1$x[,!is.na(rm1$coef)])
> set.seed(2)
> rm1c <- lmrob(y ~ x1b + x1c + x2B + x2C + x3 + x4 + x1b:x2B + x1b:x2C, data2)
>
> ## add class lm to rm1 (for now)
> class(rm1) <- c(class(rm1), "lm")
> class(rm0) <- c(class(rm0), "lm")
>
> ## the full matrix (data) should be returned by model matrix (frame)
> stopifnot(all.equal(model.matrix(cm1), model.matrix(rm1)),
+ all.equal(model.frame (cm1), model.frame (rm1)))
> ## qr decomposition should be for the full data and pivots identical lm result
> qr.cm1 <- qr(cm1)$qr
> qr.rm1 <- rm1$qr$qr
> stopifnot(NCOL(qr.rm1) == NCOL(qr.cm1),
+ NROW(qr.rm1) == NROW(qr.cm1),
+ length(rm1$qr$qraux) == length(qr(cm1)$qraux),
+ all.equal(rm1$qr$pivot, qr(cm1)$pivot),
+ all.equal(dimnames(qr.rm1),dimnames(qr.cm1)))
> ## the alias function should return the same result
> stopifnot(all.equal(alias(cm1), alias(rm1)))
>
> ####
> ## these helper functions should print NAs for the dropped coefficients
> print(rm1)
Call:
lmrob(formula = y ~ x1 * x2 + x3 + x4 + x5, data = data)
\--> method = "MM"
Coefficients:
(Intercept) x1b x1c x2B x2C x3
0.4381 0.5968 0.0344 0.2012 0.1789 -0.1320
x4 x5 x1b:x2B x1c:x2B x1b:x2C x1c:x2C
-0.2155 NA -1.8763 NA -0.8651 NA
> summary(rm1) -> s1
> confint(rm1) -> ci1
> stopifnot(identical(is.na(coef(cm1)), apply(ci1, 1L, anyNA)),
+ identical(sigma(rm1), s1$ sigma),
+ identical(vcov(rm1, complete=FALSE), s1$ cov ),
+ TRUE)
>
> print(s1, showAlgo=FALSE)
Call:
lmrob(formula = y ~ x1 * x2 + x3 + x4 + x5, data = data)
\--> method = "MM"
Residuals:
Min 1Q Median 3Q Max
-1.4584 -0.3556 0.0246 0.3651 1.0296
Coefficients: (3 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4381 0.5443 0.80 0.44
x1b 0.5968 0.6423 0.93 0.38
x1c 0.0344 0.6880 0.05 0.96
x2B 0.2012 0.7164 0.28 0.79
x2C 0.1789 0.6871 0.26 0.80
x3 -0.1320 0.4155 -0.32 0.76
x4 -0.2155 0.1694 -1.27 0.24
x5 NA NA NA NA
x1b:x2B -1.8763 1.2153 -1.54 0.16
x1c:x2B NA NA NA NA
x1b:x2C -0.8651 0.7466 -1.16 0.28
x1c:x2C NA NA NA NA
Robust residual standard error: 0.927
(3 observations deleted due to missingness)
Multiple R-squared: 0.338, Adjusted R-squared: -0.251
Convergence in 15 IRWLS iterations
Robustness weights:
2 weights are ~= 1. The remaining 16 ones are summarized as
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.787 0.937 0.985 0.952 0.988 0.994
> ci1
2.5 % 97.5 %
(Intercept) -0.79333 1.66946
x1b -0.85607 2.04973
x1c -1.52188 1.59076
x2B -1.41948 1.82189
x2C -1.37549 1.73320
x3 -1.07182 0.80783
x4 -0.59863 0.16756
x5 NA NA
x1b:x2B -4.62539 0.87283
x1c:x2B NA NA
x1b:x2C -2.55391 0.82381
x1c:x2C NA NA
> ## drop1 should return df = 0
> #drop1(rm1) ## drop.lm does not return valid results (yet)!
>
> ####
> ## methods that should just drop the NA coefficients
> ## m3 is actually the same as rm1, so anova should raise an error
> tools::assertError(anova(rm1, m3, test="Wald"))
> tools::assertError(anova(rm1, m3, test="Deviance"))
> ## but comparing rm1 and rm0 should be ok
> anova(rm1, rm0, test="Wald")
Robust Wald Test Table
Model 1: y ~ x1 * x2 + x3 + x4 + x5
Model 2: y ~ x1 * x2 + x3
Largest model fitted by lmrob(), i.e. SM
pseudoDf Test.Stat Df Pr(>chisq)
1 6
2 10 1.62 1 0.2
> anova(rm1, rm0, test="Deviance")
Robust Deviance Table
Model 1: y ~ x1 * x2 + x3 + x4 + x5
Model 2: y ~ x1 * x2 + x3
Largest model fitted by lmrob(), i.e. SM
pseudoDf Test.Stat Df Pr(>chisq)
1 6
2 10 1.4 1 0.24
> ## commands with single #:
> ## they do (or might) not return sensible results for robust fits
> ## and need to be checked again
> #cooks.distance(rm1)
> #deviance(rm1)
> #dfbeta(rm1)
> #dfbetas(rm1)
> #effects(rm1) ## fails
> #extractAIC(rm1)
> #influence(rm1)
> stopifnot(all.equal(hv1 <- hatvalues(rm1), .lmrob.hat(wqr=rm1$qr), tol=1e-15),
+ all.equal(hv1, stats:::hatvalues.lm(rm1), tol=1e-15),
+ all.equal(hat(cm1$qr), unname(hatvalues(cm1)), tol=1e-15),
+ all.equal(unname(hv1), hat(rm1$qr), tol=1e-15),
+ ## ditto :
+ all.equal(hv1c <- hatvalues(rm1c), stats:::hatvalues.lm(rm1c), tol=1e-15))
Error in match.arg(type) : 'arg' should be one of "prior", "robustness"
Calls: stopifnot ... weighted.residuals -> weights -> weights.lmrob -> match.arg
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.99-6
Check: tests
Result: ERROR
Running ‘LTS-specials.R’
Running ‘MCD-specials.R’
Comparing ‘MCD-specials.Rout’ to ‘MCD-specials.Rout.save’ ... OK
Running ‘MT-tst.R’ [12s/31s]
Running ‘NAcoef.R’
Running ‘OGK-ex.R’
Comparing ‘OGK-ex.Rout’ to ‘OGK-ex.Rout.save’ ... OK
Running ‘Qn-Sn-plots.R’
Running ‘Rsquared.R’
Comparing ‘Rsquared.Rout’ to ‘Rsquared.Rout.save’ ... OK
Running ‘binom-ni-small.R’
Comparing ‘binom-ni-small.Rout’ to ‘binom-ni-small.Rout.save’ ... OK
Running ‘binom-no-x.R’
Running ‘comedian-tst.R’
Running ‘exact-fit-categorical.R’
Running ‘glmrob-1.R’ [14s/29s]
Running ‘glmrob-specials.R’
Running ‘huber-etc.R’
Comparing ‘huber-etc.Rout’ to ‘huber-etc.Rout.save’ ... OK
Running ‘large-values.R’
Running ‘lmrob-data.R’
Running ‘lmrob-ex12.R’
Running ‘lmrob-methods.R’
Comparing ‘lmrob-methods.Rout’ to ‘lmrob-methods.Rout.save’ ... OK
Running ‘lmrob-psifns.R’ [6s/21s]
Comparing ‘lmrob-psifns.Rout’ to ‘lmrob-psifns.Rout.save’ ... OK
Running ‘m-s-estimator.R’
Running ‘mc-etc.R’
Running ‘mc-strict.R’ [27s/82s]
Running ‘nlregrob-tst.R’ [27s/75s]
Running ‘nlrob-tst.R’ [4s/11s]
Running ‘poisson-ex.R’
Running ‘psi-rho-etc.R’
Comparing ‘psi-rho-etc.Rout’ to ‘psi-rho-etc.Rout.save’ ... OK
Running ‘small-sample.R’ [14s/36s]
Comparing ‘small-sample.Rout’ to ‘small-sample.Rout.save’ ... OK
Running ‘subsample.R’ [9s/25s]
Running ‘tlts.R’
Comparing ‘tlts.Rout’ to ‘tlts.Rout.save’ ... OK
Running ‘tmcd.R’ [10s/28s]
Running ‘weights.R’
Comparing ‘weights.Rout’ to ‘weights.Rout.save’ ... OK
Running ‘wgt-himed-xtra.R’
Running ‘wgt-himed.R’
Comparing ‘wgt-himed.Rout’ to ‘wgt-himed.Rout.save’ ... OK
Running the tests in ‘tests/NAcoef.R’ failed.
Complete output:
> ## test handing of NA coefficients / singular fits
> ## also check:
> ## -- what would have to be done if class "lm" was added.
> ## -- general compatibility to class lm.
> require(robustbase)
Loading required package: robustbase
> options(digits = 5)# -> higher chance of platform independence
>
> ## generate simple example data (almost as in ./weights.R )
> data <- expand.grid(x1=letters[1:3], x2=LETTERS[1:3], rep=1:3)
> set.seed(1)
> data$y <- rnorm(nrow(data))
> ## drop all combinations of one interaction:
> data <- subset(data, x1 != 'c' | (x2 != 'B' & x2 != 'C'))
> ## add collinear variables
> data$x3 <- rnorm(nrow(data))
> data$x4 <- rnorm(nrow(data))
> data$x5 <- data$x3 + data$x4
> ## add some NA terms
> data$y[1] <- NA
> data$x4[2:3] <- NA ## to test anova
>
> ## Classical models start with 'cm', robust just with 'rm' (or just 'm'):
> cm0 <- lm (y ~ x1*x2 + x3, data)
> cm1 <- lm (y ~ x1*x2 + x3 + x4 + x5, data)
> set.seed(2)
> rm1 <- lmrob(y ~ x1*x2 + x3 + x4 + x5, data)
> m3 <- lmrob(y ~ x1*x2 + x3 + x4, data) # same column space as rm1
> rm0 <- lmrob(y ~ x1*x2 + x3, data)
>
> ## clean version of rm1 (to check predict)
> data2 <- data.frame(y=data$y[-(1:3)], rm1$x[,!is.na(rm1$coef)])
> set.seed(2)
> rm1c <- lmrob(y ~ x1b + x1c + x2B + x2C + x3 + x4 + x1b:x2B + x1b:x2C, data2)
>
> ## add class lm to rm1 (for now)
> class(rm1) <- c(class(rm1), "lm")
> class(rm0) <- c(class(rm0), "lm")
>
> ## the full matrix (data) should be returned by model matrix (frame)
> stopifnot(all.equal(model.matrix(cm1), model.matrix(rm1)),
+ all.equal(model.frame (cm1), model.frame (rm1)))
> ## qr decomposition should be for the full data and pivots identical lm result
> qr.cm1 <- qr(cm1)$qr
> qr.rm1 <- rm1$qr$qr
> stopifnot(NCOL(qr.rm1) == NCOL(qr.cm1),
+ NROW(qr.rm1) == NROW(qr.cm1),
+ length(rm1$qr$qraux) == length(qr(cm1)$qraux),
+ all.equal(rm1$qr$pivot, qr(cm1)$pivot),
+ all.equal(dimnames(qr.rm1),dimnames(qr.cm1)))
> ## the alias function should return the same result
> stopifnot(all.equal(alias(cm1), alias(rm1)))
>
> ####
> ## these helper functions should print NAs for the dropped coefficients
> print(rm1)
Call:
lmrob(formula = y ~ x1 * x2 + x3 + x4 + x5, data = data)
\--> method = "MM"
Coefficients:
(Intercept) x1b x1c x2B x2C x3
0.4381 0.5968 0.0344 0.2012 0.1789 -0.1320
x4 x5 x1b:x2B x1c:x2B x1b:x2C x1c:x2C
-0.2155 NA -1.8763 NA -0.8651 NA
> summary(rm1) -> s1
> confint(rm1) -> ci1
> stopifnot(identical(is.na(coef(cm1)), apply(ci1, 1L, anyNA)),
+ identical(sigma(rm1), s1$ sigma),
+ identical(vcov(rm1, complete=FALSE), s1$ cov ),
+ TRUE)
>
> print(s1, showAlgo=FALSE)
Call:
lmrob(formula = y ~ x1 * x2 + x3 + x4 + x5, data = data)
\--> method = "MM"
Residuals:
Min 1Q Median 3Q Max
-1.4584 -0.3556 0.0246 0.3651 1.0296
Coefficients: (3 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4381 0.5443 0.80 0.44
x1b 0.5968 0.6423 0.93 0.38
x1c 0.0344 0.6880 0.05 0.96
x2B 0.2012 0.7164 0.28 0.79
x2C 0.1789 0.6871 0.26 0.80
x3 -0.1320 0.4155 -0.32 0.76
x4 -0.2155 0.1694 -1.27 0.24
x5 NA NA NA NA
x1b:x2B -1.8763 1.2153 -1.54 0.16
x1c:x2B NA NA NA NA
x1b:x2C -0.8651 0.7466 -1.16 0.28
x1c:x2C NA NA NA NA
Robust residual standard error: 0.927
(3 observations deleted due to missingness)
Multiple R-squared: 0.338, Adjusted R-squared: -0.251
Convergence in 15 IRWLS iterations
Robustness weights:
2 weights are ~= 1. The remaining 16 ones are summarized as
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.787 0.937 0.985 0.952 0.988 0.994
> ci1
2.5 % 97.5 %
(Intercept) -0.79333 1.66946
x1b -0.85607 2.04973
x1c -1.52188 1.59076
x2B -1.41948 1.82189
x2C -1.37549 1.73320
x3 -1.07182 0.80783
x4 -0.59863 0.16756
x5 NA NA
x1b:x2B -4.62539 0.87283
x1c:x2B NA NA
x1b:x2C -2.55391 0.82381
x1c:x2C NA NA
> ## drop1 should return df = 0
> #drop1(rm1) ## drop.lm does not return valid results (yet)!
>
> ####
> ## methods that should just drop the NA coefficients
> ## m3 is actually the same as rm1, so anova should raise an error
> tools::assertError(anova(rm1, m3, test="Wald"))
> tools::assertError(anova(rm1, m3, test="Deviance"))
> ## but comparing rm1 and rm0 should be ok
> anova(rm1, rm0, test="Wald")
Robust Wald Test Table
Model 1: y ~ x1 * x2 + x3 + x4 + x5
Model 2: y ~ x1 * x2 + x3
Largest model fitted by lmrob(), i.e. SM
pseudoDf Test.Stat Df Pr(>chisq)
1 6
2 10 1.62 1 0.2
> anova(rm1, rm0, test="Deviance")
Robust Deviance Table
Model 1: y ~ x1 * x2 + x3 + x4 + x5
Model 2: y ~ x1 * x2 + x3
Largest model fitted by lmrob(), i.e. SM
pseudoDf Test.Stat Df Pr(>chisq)
1 6
2 10 1.4 1 0.24
> ## commands with single #:
> ## they do (or might) not return sensible results for robust fits
> ## and need to be checked again
> #cooks.distance(rm1)
> #deviance(rm1)
> #dfbeta(rm1)
> #dfbetas(rm1)
> #effects(rm1) ## fails
> #extractAIC(rm1)
> #influence(rm1)
> stopifnot(all.equal(hv1 <- hatvalues(rm1), .lmrob.hat(wqr=rm1$qr), tol=1e-15),
+ all.equal(hv1, stats:::hatvalues.lm(rm1), tol=1e-15),
+ all.equal(hat(cm1$qr), unname(hatvalues(cm1)), tol=1e-15),
+ all.equal(unname(hv1), hat(rm1$qr), tol=1e-15),
+ ## ditto :
+ all.equal(hv1c <- hatvalues(rm1c), stats:::hatvalues.lm(rm1c), tol=1e-15))
Error in match.arg(type) : 'arg' should be one of "prior", "robustness"
Calls: stopifnot ... weighted.residuals -> weights -> weights.lmrob -> match.arg
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc