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R version 4.3.1 beta (2023-06-07 r84521) -- "Beagle Scouts"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> ### tests methods argument of lmrob.control
>
> library(robustbase)
>
> data(stackloss)
> cat("doExtras:", doExtras <- robustbase:::doExtras(),"\n")
doExtras: FALSE
>
> str(ctrl2 <- lmrob.control(trace.lev = if(doExtras) 2 else 0))
List of 34
$ setting : NULL
$ seed : int(0)
$ nResample : num 500
$ psi : chr "bisquare"
$ tuning.chi : num 1.55
$ bb : num 0.5
$ tuning.psi : num 4.69
$ max.it : num 50
$ groups : num 5
$ n.group : num 400
$ best.r.s : int 2
$ k.fast.s : int 1
$ k.max : int 200
$ maxit.scale : int 200
$ k.m_s : int 20
$ refine.tol : num 1e-07
$ rel.tol : num 1e-07
$ scale.tol : num 1e-10
$ solve.tol : num 1e-07
$ zero.tol : num 1e-10
$ trace.lev : num 0
$ mts : int 1000
$ subsampling : chr "nonsingular"
$ compute.rd : logi FALSE
$ method : chr "MM"
$ numpoints : int 10
$ cov : chr ".vcov.avar1"
$ split.type : chr "f"
$ fast.s.large.n : num 2000
$ eps.outlier :function (nobs)
$ eps.x :function (maxx)
$ compute.outlier.stats: chr "SM"
$ warn.limit.reject : num 0.5
$ warn.limit.meanrw : num 0.5
- attr(*, "class")= chr "lmrobCtrl"
>
> ## S
> set.seed(0)
> summary(m0 <- lmrob(stack.loss ~ ., data = stackloss, method = "S",
+ compute.outlier.stats = "S"))
Call:
lmrob(formula = stack.loss ~ ., data = stackloss, method = "S", compute.outlier.stats = "S")
\--> method = "S"
Residuals:
Min 1Q Median 3Q Max
-9.46226 -0.82076 0.02249 0.80806 8.31829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -36.92542 5.41708 -6.816 3.0e-06 ***
Air.Flow 0.84957 0.07892 10.765 5.2e-09 ***
Water.Temp 0.43047 0.19507 2.207 0.0414 *
Acid.Conc. -0.07354 0.07216 -1.019 0.3224
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Robust residual standard error: 1.912
Multiple R-squared: 0.9863, Adjusted R-squared: 0.9839
Robustness weights:
5 observations c(1,3,4,13,21) are outliers with |weight| = 0 ( < 0.0048);
one weight is ~= 1. The remaining 15 ones are summarized as
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.4126 0.7595 0.8726 0.8270 0.9718 0.9986
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
4.762e-03 1.692e-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.w"
compute.outlier.stats
"S"
seed : int(0)
> set.seed(0)
> m0a <- lmrob.S(m0$x, stack.loss, ctrl2)
>
> all.equal(m0 [c('coefficients', 'scale', 'rweights')],
+ m0a[c('coefficients', 'scale', 'rweights')])
[1] TRUE
>
> ## MM
> set.seed(0)
> summary(m1 <- lmrob(stack.loss ~ ., data = stackloss, method = "MM",
+ compute.outlier.stats = "S"))
Call:
lmrob(formula = stack.loss ~ ., data = stackloss, method = "MM", compute.outlier.stats = "S")
\--> method = "MM"
Residuals:
Min 1Q Median 3Q Max
-10.50974 -1.43819 -0.09134 1.02503 7.23113
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -41.52462 5.29780 -7.838 4.82e-07 ***
Air.Flow 0.93885 0.11743 7.995 3.68e-07 ***
Water.Temp 0.57955 0.26296 2.204 0.0416 *
Acid.Conc. -0.11292 0.06989 -1.616 0.1246
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Robust residual standard error: 1.912
Multiple R-squared: 0.9593, Adjusted R-squared: 0.9521
Convergence in 17 IRWLS iterations
Robustness weights:
observation 21 is an outlier with |weight| = 0 ( < 0.0048);
2 weights are ~= 1. The remaining 18 ones are summarized as
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1215 0.8757 0.9428 0.8721 0.9797 0.9978
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
4.762e-03 1.692e-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
"S"
seed : int(0)
>
> set.seed(0)
> m2 <- update(m1, method = "SM")
>
> all.equal(m1[c('coefficients', 'scale', 'cov')],
+ m2[c('coefficients', 'scale', 'cov')])
[1] TRUE
>
> set.seed(0)
> m3 <- update(m0, method = "SM", cov = '.vcov.w')
>
> ## SMD
> set.seed(0)
> summary(m4 <- lmrob(stack.loss ~ ., data = stackloss, method = "SMD", psi = 'bisquare',
+ compute.outlier.stats = "S"))
Call:
lmrob(formula = stack.loss ~ ., data = stackloss, method = "SMD", psi = "bisquare",
compute.outlier.stats = "S")
\--> method = "SMD"
Residuals:
Min 1Q Median 3Q Max
-10.50974 -1.43819 -0.09134 1.02503 7.23113
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -41.5246 8.9525 -4.638 0.000235 ***
Air.Flow 0.9388 0.1175 7.990 3.71e-07 ***
Water.Temp 0.5796 0.3199 1.812 0.087756 .
Acid.Conc. -0.1129 0.1176 -0.960 0.350512
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Robust residual standard error: 2.651
Multiple R-squared: 0.9593, Adjusted R-squared: 0.9521
Convergence in 17 IRWLS iterations
Robustness weights:
observation 21 is an outlier with |weight| = 0 ( < 0.0048);
2 weights are ~= 1. The remaining 18 ones are summarized as
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1215 0.8757 0.9428 0.8721 0.9797 0.9978
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
4.762e-03 1.692e-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 numpoints
200 0 1000 0 10
fast.s.large.n
2000
psi subsampling cov
"bisquare" "nonsingular" ".vcov.w"
compute.outlier.stats
"S"
seed : int(0)
> summary(m4a <- lmrob..D..fit(m3))
Call:
lmrob(formula = stack.loss ~ ., data = stackloss, method = "SMD", compute.outlier.stats = "S",
cov = ".vcov.w")
\--> method = "MM"
Residuals:
Min 1Q Median 3Q Max
-10.50974 -1.43819 -0.09134 1.02503 7.23113
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -41.5246 9.3676 -4.433 0.000365 ***
Air.Flow 0.9388 0.1230 7.636 6.84e-07 ***
Water.Temp 0.5796 0.3348 1.731 0.101505
Acid.Conc. -0.1129 0.1231 -0.917 0.371736
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Robust residual standard error: 2.651
Multiple R-squared: 0.9593, Adjusted R-squared: 0.9521
Convergence in 17 IRWLS iterations
Robustness weights:
observation 21 is an outlier with |weight| = 0 ( < 0.0048);
2 weights are ~= 1. The remaining 18 ones are summarized as
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1215 0.8757 0.9428 0.8721 0.9797 0.9978
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
4.762e-03 1.692e-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.w"
compute.outlier.stats
"S"
seed : int(0)
>
> ## rearrange m4a and update call
> m4a <- m4a[names(m4)]
> class(m4a) <- class(m4)
> m4a$call <- m4$call
>
> all.equal(m4, m4a, check.environment = FALSE)
[1] "Component \"control\": Component \"method\": 1 string mismatch"
[2] "Component \"init\": Component \"control\": Component \"method\": 1 string mismatch"
[3] "Component \"cov\": Attributes: < Component \"corrfact\": Mean relative difference: 0.1167673 >"
[4] "Component \"cov\": Attributes: < Component \"scorr\": Mean relative difference: 0.01959345 >"
[5] "Component \"cov\": Mean relative difference: 0.09488594"
>
> ## SMDM
> set.seed(0)
> summary(m5 <- lmrob(stack.loss ~ ., data = stackloss, method = "SMDM", psi = 'bisquare',
+ compute.outlier.stats = "S"))
Call:
lmrob(formula = stack.loss ~ ., data = stackloss, method = "SMDM", psi = "bisquare",
compute.outlier.stats = "S")
\--> method = "SMDM"
Residuals:
Min 1Q Median 3Q Max
-9.6746 -1.7721 0.1346 1.2041 6.6080
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -41.9398 9.7719 -4.292 0.000494 ***
Air.Flow 0.8747 0.1231 7.107 1.76e-06 ***
Water.Temp 0.8099 0.3363 2.408 0.027656 *
Acid.Conc. -0.1188 0.1284 -0.926 0.367655
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Robust residual standard error: 2.651
Multiple R-squared: 0.9384, Adjusted R-squared: 0.9275
Convergence in 17 IRWLS iterations
Robustness weights:
2 weights are ~= 1. The remaining 19 ones are summarized as
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1546 0.9139 0.9597 0.8874 0.9866 0.9966
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
4.762e-03 1.692e-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 numpoints
200 0 1000 0 10
fast.s.large.n
2000
psi subsampling cov
"bisquare" "nonsingular" ".vcov.w"
compute.outlier.stats
"S"
seed : int(0)
> summary(m5a <- lmrob..M..fit(obj=m4))
Call:
lmrob(formula = stack.loss ~ ., data = stackloss, method = "SMDM", psi = "bisquare",
compute.outlier.stats = "S")
\--> method = "SMD"
Residuals:
Min 1Q Median 3Q Max
-9.6746 -1.7721 0.1346 1.2041 6.6080
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -41.9398 9.7719 -4.292 0.000494 ***
Air.Flow 0.8747 0.1231 7.107 1.76e-06 ***
Water.Temp 0.8099 0.3363 2.408 0.027656 *
Acid.Conc. -0.1188 0.1284 -0.926 0.367655
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Robust residual standard error: 2.651
Multiple R-squared: 0.9384, Adjusted R-squared: 0.9275
Convergence in 17 IRWLS iterations
Robustness weights:
2 weights are ~= 1. The remaining 19 ones are summarized as
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1546 0.9139 0.9597 0.8874 0.9866 0.9966
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
4.762e-03 1.692e-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 numpoints
200 0 1000 0 10
fast.s.large.n
2000
psi subsampling cov
"bisquare" "nonsingular" ".vcov.w"
compute.outlier.stats
"S"
seed : int(0)
>
> ## rearrange m5a
> m5a <- m5a[names(m5)]
> class(m5a) <- class(m5)
>
> all.equal(m5, m5a, check.environment = FALSE) #-> 3 string mismatch
[1] "Component \"control\": Component \"method\": 1 string mismatch"
[2] "Component \"init\": Component \"control\": Component \"method\": 1 string mismatch"
[3] "Component \"init\": Component \"init\": Component \"control\": Component \"method\": 1 string mismatch"
>
> ## Fast S large n strategy (sped up)
> model <- model.frame(LNOx ~ . ,data = NOxEmissions)
> control <- lmrob.control(fast.s.large.n = 10, n.group = 341, groups = 2)
> set.seed(0)
> try(ret <- lmrob.S(model.matrix(model, NOxEmissions)[1:682,], NOxEmissions$LNOx[1:682], control))
Error in lmrob.S(model.matrix(model, NOxEmissions)[1:682, ], NOxEmissions$LNOx[1:682], :
Fast S large n strategy failed. Use control parameter 'fast.s.large.n = Inf'.
In addition: Warning message:
In lmrob.S(model.matrix(model, NOxEmissions)[1:682, ], NOxEmissions$LNOx[1:682], :
'control$n.group' is not much larger than 'p', probably too small
> ## do what the error says
> control <- lmrob.control(fast.s.large.n = Inf)
> try(ret <- lmrob.S(model.matrix(model, NOxEmissions)[1:682,], NOxEmissions$LNOx[1:682], control))
Error in lmrob.S(model.matrix(model, NOxEmissions)[1:682, ], NOxEmissions$LNOx[1:682], :
DGEEQU: column 30 of the design matrix is exactly zero.
> ##-> Error ...... DGEEQU: column 30 of the design matrix is exactly zero.
> ##
> ## still fails, but this error is to be expected since only a part
> ## of the design matrix is given
>
> proc.time()
user system elapsed
0.253 0.085 0.376
|
