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View Code? Open in Web Editor NEWR package robust: Port of the S+ "Robust Library"
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One example fails: Error: data.matrix(woodmod.dat) == data.matrix(wood[, 1:5]) are not all TRUE
R version 4.2.3 (2023-03-15) -- "Shortstop Beagle"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: powerpc-apple-darwin10.8.0 (32-bit)
> pkgname <- "robust"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('robust')
Loading required package: fit.models
>
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("OverlaidDenPlot.fdfm")
> ### * OverlaidDenPlot.fdfm
>
> flush(stderr()); flush(stdout())
>
> ### Name: overlaidDenPlot.fdfm
> ### Title: Overlaid Density Plot
> ### Aliases: overlaidDenPlot.fdfm
> ### Keywords: hplot
>
> ### ** Examples
>
> data(los, package="robustbase")
>
> ## Not run:
> ##D
> ##D los.fm <- fit.models(c(Robust = "fitdstnRob", MLE = "fitdstn"),
> ##D x = los, densfun = "gamma")
> ##D
> ##D
> ##D los.fm <- fit.models(c(Robust = "fitdstnRob", MLE = "fitdstn"),
> ##D x = los, densfun = "weibull")
> ##D
> ##D overlaidDenPlot.fdfm(los.fm, xlab = "x-axis label", ylab = "y-axis label",
> ##D main = "Plot Title")
> ##D
> ## End(Not run)
>
>
>
> cleanEx()
> nameEx("anova.glmRob")
> ### * anova.glmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: anova.glmRob
> ### Title: ANOVA for Robust Generalized Linear Model Fits
> ### Aliases: anova.glmRob anova.glmRoblist
> ### Keywords: robust regression methods
>
> ### ** Examples
>
> data(breslow.dat)
>
> bres.int <- glmRob(sumY ~ Age10 + Base4*Trt, family = poisson(), data = breslow.dat)
> anova(bres.int)
Analysis of Deviance Table
poisson model
Response: sumY
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev
NULL 58 11983.1
Age10 1 9125.7 57 2857.5
Base4 1 803.0 56 2054.5
Trt 1 -884.6 55 2939.1
Base4:Trt 1 -949.1 54 3888.2
>
> bres.main <- glmRob(sumY ~ Age10 + Base4 + Trt, family = poisson(), data = breslow.dat)
> anova(bres.main, bres.int)
Terms Resid. Df Resid. Dev Test Df Deviance
1 Age10 + Base4 + Trt 55 2939.072 NA NA
2 Age10 + Base4 * Trt 54 3888.204 +Base4:Trt 1 -949.1315
>
>
>
> cleanEx()
> nameEx("anova.lmRob")
> ### * anova.lmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: anova.lmRob
> ### Title: ANOVA for Robust Linear Model Fits
> ### Aliases: anova.lmRob anova.lmRoblist
> ### Keywords: robust regression methods
>
> ### ** Examples
>
> data(stack.dat)
> stack.small <- lmRob(Loss ~ Water.Temp + Acid.Conc., data = stack.dat)
> stack.full <- lmRob(Loss ~ ., data = stack.dat)
> anova(stack.full)
Terms added sequentially (first to last)
Chisq Df RobustF Pr(F)
(Intercept) 1
Air.Flow 1 41.228 6.026e-11 ***
Water.Temp 1 6.522 0.009257 **
Acid.Conc. 1 0.551 0.449386
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> anova(stack.full, stack.small)
Response: Loss
Terms Df RobustF Pr(F)
[1,] 1 1
[2,] 2 1 1 27.354 9.839e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
> cleanEx()
> nameEx("breslow.dat")
> ### * breslow.dat
>
> flush(stderr()); flush(stdout())
>
> ### Name: breslow.dat
> ### Title: Breslow Data
> ### Aliases: breslow.dat
> ### Keywords: datasets
>
> ### ** Examples
>
> data(breslow.dat)
>
>
>
> cleanEx()
> nameEx("covClassic")
> ### * covClassic
>
> flush(stderr()); flush(stdout())
>
> ### Name: covClassic
> ### Title: Classical Covariance Estimation
> ### Aliases: covClassic
> ### Keywords: robust multivariate
>
> ### ** Examples
>
> data(stack.dat)
> covClassic(stack.dat)
Call:
covClassic(data = stack.dat)
Classical Estimate of Covariance:
Loss Air.Flow Water.Temp Acid.Conc.
Loss 103.46 85.76 28.148 21.793
Air.Flow 85.76 84.06 22.657 24.571
Water.Temp 28.15 22.66 9.990 6.621
Acid.Conc. 21.79 24.57 6.621 28.714
Classical Estimate of Location:
Loss Air.Flow Water.Temp Acid.Conc.
17.52 60.43 21.10 86.29
>
>
>
> cleanEx()
> nameEx("covRob")
> ### * covRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: covRob
> ### Title: Robust Covariance/Correlation Matrix Estimation
> ### Aliases: covRob
> ### Keywords: multivariate robust
>
> ### ** Examples
>
> data(stackloss)
> covRob(stackloss)
Call:
covRob(data = stackloss)
Robust Estimate of Covariance:
Air.Flow Water.Temp Acid.Conc. stack.loss
Air.Flow 33.93 11.203 22.135 29.41
Water.Temp 11.20 8.298 8.794 12.03
Acid.Conc. 22.14 8.794 37.887 17.60
stack.loss 29.41 12.030 17.605 28.17
Robust Estimate of Location:
Air.Flow Water.Temp Acid.Conc. stack.loss
56.92 20.43 86.29 13.73
>
>
>
> cleanEx()
> nameEx("covRob.control")
> ### * covRob.control
>
> flush(stderr()); flush(stdout())
>
> ### Name: covRob.control
> ### Title: Control Parameters for Robust Covariance Estimation
> ### Aliases: covRob.control
> ### Keywords: utilities
>
> ### ** Examples
>
> mcd.control <- covRob.control("mcd", quan = 0.75, ntrial = 1000)
>
> ds.control <- covRob.control("donostah", prob = 0.95)
>
> qc.control <- covRob.control("pairwiseqc")
>
>
>
> cleanEx()
> nameEx("ddPlot.covfm")
> ### * ddPlot.covfm
>
> flush(stderr()); flush(stdout())
>
> ### Name: ddPlot.covfm
> ### Title: Distance - Distance Plot
> ### Aliases: ddPlot.covfm
> ### Keywords: hplot
>
> ### ** Examples
>
> data(woodmod.dat)
> woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"),
+ data = woodmod.dat)
> ddPlot.covfm(woodm.fm, main = "Plot Title", xlab = "x-axis label",
+ ylab = "y-axis label", pch = 4, col = "purple")
>
>
>
> cleanEx()
> nameEx("distancePlot.covfm")
> ### * distancePlot.covfm
>
> flush(stderr()); flush(stdout())
>
> ### Name: distancePlot.covfm
> ### Title: Side-by-Side Mahalanobis Distance Plot
> ### Aliases: distancePlot.covfm
> ### Keywords: hplot
>
> ### ** Examples
>
> data(woodmod.dat)
> woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"),
+ data = woodmod.dat)
> distancePlot.covfm(woodm.fm, main = "Plot Title", xlab = "x-axis label",
+ ylab = "y-axis label", pch = 4, col = "purple")
>
>
>
> cleanEx()
> nameEx("drop1.lmRob")
> ### * drop1.lmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: drop1.lmRob
> ### Title: Compute an Anova Object by Dropping Terms
> ### Aliases: drop1.lmRob
> ### Keywords: robust regression methods
>
> ### ** Examples
>
> data(stack.dat)
> stack.rob <- lmRob(Loss ~ ., data = stack.dat)
> drop1(stack.rob)
Single term deletions
Model:
Loss ~ Air.Flow + Water.Temp + Acid.Conc.
scale: 1.837073
Df RFPE
<none> 16.032
Air.Flow 1 36.213
Water.Temp 1 20.829
Acid.Conc. 1 16.049
>
>
>
> cleanEx()
> nameEx("ellipsesPlot.covfm")
> ### * ellipsesPlot.covfm
>
> flush(stderr()); flush(stdout())
>
> ### Name: ellipsesPlot.covfm
> ### Title: Ellipses Plot - Visual Correlation Matrix Comparison
> ### Aliases: ellipsesPlot.covfm
> ### Keywords: hplot
>
> ### ** Examples
>
> data(woodmod.dat)
> woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"),
+ data = woodmod.dat)
> ellipsesPlot.covfm(woodm.fm)
>
>
>
> cleanEx()
> nameEx("glmRob")
> ### * glmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: glmRob
> ### Title: Fit a Robust Generalized Linear Model
> ### Aliases: glmRob
> ### Keywords: robust regression models
>
> ### ** Examples
>
> data(breslow.dat)
>
> glmRob(sumY ~ Age10 + Base4*Trt, family = poisson(),
+ data = breslow.dat, method = "cubif")
Call:
glmRob(formula = sumY ~ Age10 + Base4 * Trt, family = poisson(),
data = breslow.dat, method = "cubif")
Coefficients:
(Intercept) Age10 Base4 Trtprogabide
1.83516 0.12081 0.13915 -0.39279
Base4:Trtprogabide
0.02182
Degrees of Freedom: 59 Total; 54 Residual
Residual Deviance: 3888
>
>
>
> cleanEx()
> nameEx("glmRob.mallows")
> ### * glmRob.mallows
>
> flush(stderr()); flush(stdout())
>
> ### Name: glmRob.mallows
> ### Title: Mallows Type Estimator
> ### Aliases: glmRob.mallows
> ### Keywords: robust regression
>
> ### ** Examples
>
> data(mallows.dat)
>
> glmRob(y ~ a + b + c, data = mallows.dat, family = binomial(), method = 'mallows')
Call:
glmRob(formula = y ~ a + b + c, family = binomial(), data = mallows.dat,
method = "mallows")
Coefficients:
(Intercept) a b c
-2.04642 0.10810 0.08633 0.12584
Degrees of Freedom: 70 Total; 66 Residual
Residual Deviance: 40.06
>
>
>
> cleanEx()
> nameEx("glmRob.misclass")
> ### * glmRob.misclass
>
> flush(stderr()); flush(stdout())
>
> ### Name: glmRob.misclass
> ### Title: Consistent Misclassification Estimator
> ### Aliases: glmRob.misclass
> ### Keywords: robust regression
>
> ### ** Examples
>
> data(leuk.dat)
>
> glmRob(y ~ ag + wbc, data = leuk.dat, family = binomial(), method = 'misclass')
Call:
glmRob(formula = y ~ ag + wbc, family = binomial(), data = leuk.dat,
method = "misclass")
Coefficients:
(Intercept) ag wbc
-1.265e+00 2.219e+00 -3.276e-05
Degrees of Freedom: 33 Total; 30 Residual
Residual Deviance: 29.59
>
>
>
> cleanEx()
> nameEx("leuk.dat")
> ### * leuk.dat
>
> flush(stderr()); flush(stdout())
>
> ### Name: leuk.dat
> ### Title: Leuk Data
> ### Aliases: leuk.dat
> ### Keywords: datasets
>
> ### ** Examples
>
> data(leuk.dat)
>
>
>
> cleanEx()
> nameEx("lmRob")
> ### * lmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: lmRob
> ### Title: High Breakdown and High Efficiency Robust Linear Regression
> ### Aliases: lmRob
> ### Keywords: robust regression models
>
> ### ** Examples
>
> data(stack.dat)
> stack.rob <- lmRob(Loss ~ ., data = stack.dat)
>
>
>
> cleanEx()
> nameEx("lmRob.RFPE")
> ### * lmRob.RFPE
>
> flush(stderr()); flush(stdout())
>
> ### Name: lmRob.RFPE
> ### Title: Robust Final Prediction Errors
> ### Aliases: lmRob.RFPE
> ### Keywords: robust regression
>
> ### ** Examples
>
> data(stack.dat)
> stack.rob <- lmRob(Loss ~ ., data = stack.dat)
> lmRob.RFPE(stack.rob)
[1] 16.03201
>
>
>
> cleanEx()
> nameEx("lmRob.control")
> ### * lmRob.control
>
> flush(stderr()); flush(stdout())
>
> ### Name: lmRob.control
> ### Title: Control Parameters for Robust Linear Regression
> ### Aliases: lmRob.control
> ### Keywords: robust regression
>
> ### ** Examples
>
> data(stack.dat)
> my.control <- lmRob.control(weight=c("Bisquare","Optimal"))
> stack.bo <- lmRob(Loss ~ ., data = stack.dat, control = my.control)
>
>
>
> cleanEx()
> nameEx("lsRobTest")
> ### * lsRobTest
>
> flush(stderr()); flush(stdout())
>
> ### Name: lsRobTest
> ### Title: Bias Test for Least-Squares Regression Estimates
> ### Aliases: lsRobTest
> ### Keywords: robust regression
>
> ### ** Examples
>
> rob.fit <- lmRob(stack.loss ~ ., data = stackloss)
> lsRobTest(rob.fit)
Test for least squares bias
H0: composite normal/non-normal regression error distribution
Individual coefficient tests:
LS Robust Delta Std. Error Stat p-value
Air.Flow 0.7156 0.79769 -0.08205 0.1353 -0.6064 0.54427
Water.Temp 1.2953 0.57734 0.71795 0.3366 2.1332 0.03291
Acid.Conc. -0.1521 -0.06706 -0.08506 0.1200 -0.7091 0.47824
Joint test for bias:
Test statistic: 6.61 on 3 DF, p-value: 0.08541
> lsRobTest(rob.fit, test = "T1")
Test for least squares bias
H0: normal regression error distribution
Individual coefficient tests:
LS Robust Delta Std. Error Stat p-value
Air.Flow 0.7156 0.79769 -0.08205 0.02101 -3.906 9.388e-05
Water.Temp 1.2953 0.57734 0.71795 0.05225 13.741 5.764e-43
Acid.Conc. -0.1521 -0.06706 -0.08506 0.01862 -4.568 4.928e-06
Joint test for bias:
Test statistic: 274.3 on 3 DF, p-value: 0
>
>
>
> cleanEx()
> nameEx("mallows.dat")
> ### * mallows.dat
>
> flush(stderr()); flush(stdout())
>
> ### Name: mallows.dat
> ### Title: Mallows Data
> ### Aliases: mallows.dat
> ### Keywords: datasets
>
> ### ** Examples
>
> data(mallows.dat)
>
>
>
> cleanEx()
> nameEx("plot.covfm")
> ### * plot.covfm
>
> flush(stderr()); flush(stdout())
>
> ### Name: plot.covfm
> ### Title: Plot Method
> ### Aliases: plot.covfm plot.covRob plot.covClassic
> ### Keywords: methods hplot
>
> ### ** Examples
>
> data(woodmod.dat)
>
> woodm.cov <- covClassic(woodmod.dat)
> woodm.covRob <- covRob(woodmod.dat)
>
> plot(woodm.cov)
> plot(woodm.covRob)
>
> woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"),
+ data = woodmod.dat)
> plot(woodm.fm)
>
>
>
> cleanEx()
> nameEx("plot.fdfm")
> ### * plot.fdfm
>
> flush(stderr()); flush(stdout())
>
> ### Name: plot.fdfm
> ### Title: fdfm Plot Method
> ### Aliases: plot.fdfm
> ### Keywords: hplot
>
> ### ** Examples
>
> data(los, package = "robustbase")
> los.fm <- fit.models(c(Robust = "fitdstnRob", MLE = "fitdstn"),
+ x = los, densfun = "gamma")
> plot(los.fm)
>
>
>
> cleanEx()
> nameEx("plot.lmRob")
> ### * plot.lmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: plot.lmRob
> ### Title: Diagnostic Regression Plots
> ### Aliases: plot.lmRob
> ### Keywords: methods hplot
>
> ### ** Examples
>
> data(stack.dat)
> stack.rob <- lmRob(Loss ~ ., data = stack.dat)
> plot(stack.rob, which.plots = 6)
>
>
>
> cleanEx()
> nameEx("predict.glmRob")
> ### * predict.glmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: predict.glmRob
> ### Title: Predict Method for Robust Generalized Linear Model Fits
> ### Aliases: predict.glmRob
> ### Keywords: robust regression methods
>
> ### ** Examples
>
> data(breslow.dat)
> bres.rob <- glmRob(sumY ~ Age10 + Base4 * Trt, family = poisson(), data = breslow.dat)
> predict(bres.rob)
1 2 3 4 5 6 7 8
2.592342 2.580261 2.345916 2.548386 4.396928 3.124781 2.627130 4.151528
9 10 11 12 13 14 15 16
3.082281 2.521311 4.079040 3.273100 2.739205 3.731164 5.175793 3.888652
17 18 19 20 21 22 23 24
2.799611 6.071102 2.847937 2.784617 2.602967 2.401954 2.813149 3.111243
25 26 27 28 29 30 31 32
4.110915 2.631499 2.412579 3.735964 4.718229 3.358170 2.448596 2.207231
33 34 35 36 37 38 39 40
2.424433 2.698131 3.052314 2.428606 2.211229 4.380213 3.358083 2.062342
41 42 43 44 45 46 47 48
2.605566 2.448770 3.692188 3.144792 3.394414 2.026098 3.205198 2.187066
49 50 51 52 53 54 55 56
7.784706 2.714298 3.394327 3.152962 3.949632 2.903514 2.472846 2.641810
57 58 59
2.702129 2.400445 2.372284
>
>
>
> cleanEx()
> nameEx("predict.lmRob")
> ### * predict.lmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: predict.lmRob
> ### Title: Use predict() on an lmRob Object
> ### Aliases: predict.lmRob
> ### Keywords: robust regression methods
>
> ### ** Examples
>
> data(stack.dat)
> stack.rob <- lmRob(Loss ~ ., data = stack.dat)
> predict(stack.rob)
1 2 3 4 5 6 7 8
35.782223 35.849283 30.572054 19.825981 18.671300 19.248641 19.423620 19.423620
9 10 11 12 13 14 15 16
16.057899 13.640618 13.037076 12.526796 13.506497 13.346176 6.655592 6.856772
17 18 19 20 21
8.372955 7.903534 8.413814 13.065807 23.629863
> predict(stack.rob, newdata = stack.dat[c(1,2,4,21), ], se.fit = TRUE)
$fit
1 2 4 21
35.78222 35.84928 19.82598 23.62986
$se.fit
1 2 4 21
1.0869527 1.1059685 0.5557136 0.9526912
$residual.scale
[1] 1.837073
$df
[1] 17
>
>
>
> cleanEx()
> nameEx("qqPlot.fdfm")
> ### * qqPlot.fdfm
>
> flush(stderr()); flush(stdout())
>
> ### Name: qqPlot.fdfm
> ### Title: Comparison Quantile-Quantile Plot
> ### Aliases: qqPlot.fdfm
> ### Keywords: hplot
>
> ### ** Examples
>
> data(los, package = "robustbase")
> los.fm <- fit.models(c(Robust = "fitdstnRob", MLE = "fitdstn"),
+ x = los, densfun = "gamma")
> qqPlot.fdfm(los.fm, xlab = "x-axis label", ylab = "y-axis label",
+ main = "Plot Title", pch = 4, col = "purple")
>
>
>
> cleanEx()
> nameEx("screePlot.covfm")
> ### * screePlot.covfm
>
> flush(stderr()); flush(stdout())
>
> ### Name: screePlot.covfm
> ### Title: Comparison Screeplot
> ### Aliases: screePlot.covfm
> ### Keywords: hplot
>
> ### ** Examples
>
> data(woodmod.dat)
> woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"),
+ data = woodmod.dat)
> screePlot.covfm(woodm.fm, main = "Plot Title", xlab = "x-axis label",
+ ylab = "y-axis label", pch = 4:5)
>
>
>
> cleanEx()
> nameEx("stack.dat")
> ### * stack.dat
>
> flush(stderr()); flush(stdout())
>
> ### Name: stack.dat
> ### Title: Brownlee's Stack-Loss Data
> ### Aliases: stack.dat
> ### Keywords: datasets
>
> ### ** Examples
>
> data(stack.dat)
> stack.dat
Loss Air.Flow Water.Temp Acid.Conc.
1 42 80 27 89
2 37 80 27 88
3 37 75 25 90
4 28 62 24 87
5 18 62 22 87
6 18 62 23 87
7 19 62 24 93
8 20 62 24 93
9 15 58 23 87
10 14 58 18 80
11 14 58 18 89
12 13 58 17 88
13 11 58 18 82
14 12 58 19 93
15 8 50 18 89
16 7 50 18 86
17 8 50 19 72
18 8 50 19 79
19 9 50 20 80
20 15 56 20 82
21 15 70 20 91
>
>
>
> cleanEx()
> nameEx("step.lmRob")
> ### * step.lmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: step.lmRob
> ### Title: Build a Model in a Stepwise Fashion
> ### Aliases: step.lmRob
> ### Keywords: robust regression methods
>
> ### ** Examples
>
> data(stack.dat)
> stack.rob <- lmRob(Loss ~ ., data = stack.dat)
>
> ## The default behavior is to try dropping all terms ##
> step.lmRob(stack.rob)
Start: RFPE= 16.032
Loss ~ Air.Flow + Water.Temp + Acid.Conc.
Single term deletions
Model:
Loss ~ Air.Flow + Water.Temp + Acid.Conc.
scale: 1.837073
Df RFPE
<none> 16.032
Air.Flow 1 36.213
Water.Temp 1 20.829
Acid.Conc. 1 16.049
Call:
lmRob(formula = Loss ~ ., data = stack.dat)
Coefficients:
(Intercept) Air.Flow Water.Temp Acid.Conc.
-37.65246 0.79769 0.57734 -0.06706
>
> ## Keep Water.Temp in the model ##
> my.scope <- list(lower = . ~ Water.Temp, upper = . ~ .)
> step.lmRob(stack.rob, scope = my.scope)
Start: RFPE= 16.032
Loss ~ Air.Flow + Water.Temp + Acid.Conc.
Single term deletions
Model:
Loss ~ Air.Flow + Water.Temp + Acid.Conc.
scale: 1.837073
Df RFPE
<none> 16.032
Air.Flow 1 36.213
Acid.Conc. 1 16.049
Call:
lmRob(formula = Loss ~ ., data = stack.dat)
Coefficients:
(Intercept) Air.Flow Water.Temp Acid.Conc.
-37.65246 0.79769 0.57734 -0.06706
>
>
>
> cleanEx()
> nameEx("summary.covfm")
> ### * summary.covfm
>
> flush(stderr()); flush(stdout())
>
> ### Name: summary.covfm
> ### Title: Summary Method
> ### Aliases: summary.covClassic summary.covRob summary.covfm
> ### Keywords: methods
>
> ### ** Examples
>
> data(woodmod.dat)
> woodm.cov <- covClassic(woodmod.dat)
> ## IGNORE_RDIFF_BEGIN
> summary(woodm.cov)
Call:
covClassic(data = woodmod.dat)
Classical Estimate of Covariance:
V1 V2 V3 V4 V5
V1 0.0082920 -0.0002912 0.003602 0.0026908 -0.0028684
V2 -0.0002912 0.0004888 -0.000352 -0.0008388 0.0006124
V3 0.0036022 -0.0003520 0.004185 0.0015788 -0.0016916
V4 0.0026908 -0.0008388 0.001579 0.0039462 -0.0007920
V5 -0.0028684 0.0006124 -0.001692 -0.0007920 0.0027570
Classical Estimate of Location:
V1 V2 V3 V4 V5
0.5509 0.1330 0.5087 0.5112 0.9070
Eigenvalues:
Eval. 1 Eval. 2 Eval. 3 Eval. 4 Eval. 5
0.0128527 0.0029621 0.0021125 0.0016344 0.0001075
Squared Mahalanobis Distances:
1 2 3 4 5 6 7 8 9 10 11 12 13
4.327 1.552 3.224 3.959 3.277 3.974 9.124 4.536 5.665 7.588 5.075 6.833 4.506
14 15 16 17 18 19 20
1.500 1.945 9.049 4.548 4.637 4.599 5.084
> ## IGNORE_RDIFF_END
>
> woodm.covRob <- covRob(woodmod.dat)
> summary(woodm.covRob)
Call:
covRob(data = woodmod.dat)
Robust Estimate of Covariance:
V1 V2 V3 V4 V5
V1 0.038232 0.0066282 -0.0021650 -0.0015136 -0.0048570
V2 0.006628 0.0016512 0.0001382 -0.0010400 -0.0003837
V3 -0.002165 0.0001382 0.0036709 0.0001514 0.0015113
V4 -0.001514 -0.0010400 0.0001514 0.0048313 -0.0014409
V5 -0.004857 -0.0003837 0.0015113 -0.0014409 0.0044166
Robust Estimate of Location:
V1 V2 V3 V4 V5
0.5693 0.1189 0.5093 0.5399 0.8964
Eigenvalues:
Eval. 1 Eval. 2 Eval. 3 Eval. 4 Eval. 5
0.0402612 0.0063495 0.0039998 0.0019171 0.0002747
Squared Robust Distances:
[1] 1.2996 0.3348 0.4099 15.8192 0.4578 18.0052 8.5876 24.2857 8.1617
[10] 5.1665 2.0412 5.3157 5.7099 0.2672 0.3173 5.5845 3.4097 0.4362
[19] 25.9520 3.5364
>
> woodm.fm <- fit.models(list(Robust = "covRob", Classical = "covClassic"),
+ data = woodmod.dat)
> summary(woodm.fm)
Calls:
Robust: covRob(data = woodmod.dat)
Classical: covClassic(data = woodmod.dat)
Comparison of Covariance/Correlation Estimates:
(unique correlation terms)
[1,1] [2,1] [3,1] [4,1] [5,1] [2,2]
Robust 0.038232 0.0066282 -0.002165 -0.001514 -0.004857 0.0016512
Classical 0.008292 -0.0002912 0.003602 0.002691 -0.002868 0.0004888
[3,2] [4,2] [5,2] [3,3] [4,3] [5,3]
Robust 0.0001382 -0.0010400 -0.0003837 0.003671 0.0001514 0.001511
Classical -0.0003520 -0.0008388 0.0006124 0.004185 0.0015788 -0.001692
[4,4] [5,4] [5,5]
Robust 0.004831 -0.001441 0.004417
Classical 0.003946 -0.000792 0.002757
Comparison of center Estimates:
V1 V2 V3 V4 V5
Robust 0.5693 0.1189 0.5093 0.5399 0.8964
Classical 0.5509 0.1330 0.5087 0.5112 0.9070
Comparison of Eigenvalues:
Eval. 1 Eval. 2 Eval. 3 Eval. 4 Eval. 5
Robust 0.04026 0.006349 0.004000 0.001917 0.0002747
Classical 0.01285 0.002962 0.002112 0.001634 0.0001075
>
>
>
> cleanEx()
> nameEx("summary.glmRob")
> ### * summary.glmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: summary.glmRob
> ### Title: Summarizing Robust Generalized Linear Model Fits
> ### Aliases: summary.glmRob
> ### Keywords: methods robust regression
>
> ### ** Examples
>
> data(breslow.dat)
> bres.rob <- glmRob(sumY ~ Age10 + Base4*Trt, family = poisson(), data = breslow.dat)
> bres.sum <- summary(bres.rob)
> bres.sum
Call: glmRob(formula = sumY ~ Age10 + Base4 * Trt, family = poisson(),
data = breslow.dat)
Deviance Residuals:
Min 1Q Median 3Q Max
-54.31624 -1.48734 0.04103 0.87948 8.92507
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.83516 0.28542 6.4296 1.279e-10
Age10 0.12081 0.07495 1.6118 1.070e-01
Base4 0.13915 0.03541 3.9298 8.501e-05
Trtprogabide -0.39279 0.22101 -1.7772 7.554e-02
Base4:Trtprogabide 0.02182 0.04003 0.5451 5.857e-01
(Dispersion Parameter for poisson family taken to be 1 )
Null Deviance: 11983 on 58 degrees of freedom
Residual Deviance: 3888.204 on 54 degrees of freedom
Number of Iterations: 9
Correlation of Coefficients:
(Intercept) Age10 Base4 Trtprogabide
Age10 -0.80956
Base4 -0.62030 0.10855
Trtprogabide -0.46447 0.02404 0.69012
Base4:Trtprogabide 0.52264 -0.06402 -0.88082 -0.89436
>
>
>
> cleanEx()
> nameEx("summary.lmRob")
> ### * summary.lmRob
>
> flush(stderr()); flush(stdout())
>
> ### Name: summary.lmRob
> ### Title: Summarizing Robust Linear Model Fits
> ### Aliases: summary.lmRob
> ### Keywords: methods robust regression
>
> ### ** Examples
>
> data(stack.dat)
> stack.rob <- lmRob(Loss ~ ., data = stack.dat)
> stack.sum <- summary(stack.rob)
> stack.sum
Call:
lmRob(formula = Loss ~ ., data = stack.dat)
Residuals:
Min 1Q Median 3Q Max
-8.6299 -0.6713 0.3594 1.1507 8.1740
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -37.65246 5.00256 -7.527 8.29e-07 ***
Air.Flow 0.79769 0.07129 11.189 2.91e-09 ***
Water.Temp 0.57734 0.17546 3.291 0.00432 **
Acid.Conc. -0.06706 0.06512 -1.030 0.31757
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.837 on 17 degrees of freedom
Multiple R-Squared: 0.6205
Test for Bias:
statistic p-value
M-estimate 2.751 0.6004
LS-estimate 2.640 0.6197
> stack.bse <- summary(stack.rob, bootstrap.se = TRUE)
> stack.bse
Call:
lmRob(formula = Loss ~ ., data = stack.dat)
Residuals:
Min 1Q Median 3Q Max
-8.6299 -0.6713 0.3594 1.1507 8.1740
Coefficients:
Estimate Std. Error Bootstrap SE t value Pr(>|t|)
(Intercept) -37.65246 5.00256 4.43790 -7.527 8.29e-07 ***
Air.Flow 0.79769 0.07129 0.05086 11.189 2.91e-09 ***
Water.Temp 0.57734 0.17546 0.13551 3.291 0.00432 **
Acid.Conc. -0.06706 0.06512 0.05842 -1.030 0.31757
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.837 on 17 degrees of freedom
Multiple R-Squared: 0.6205
Test for Bias:
statistic p-value
M-estimate 2.751 0.6004
LS-estimate 2.640 0.6197
>
>
>
> cleanEx()
> nameEx("weight.funs")
> ### * weight.funs
>
> flush(stderr()); flush(stdout())
>
> ### Name: weight.funs
> ### Title: Weight Functions Psi, Rho, Chi
> ### Aliases: psi.weight rho.weight psp.weight chi.weight
> ### Keywords: robust
>
> ### ** Examples
>
> x <- seq(-4,4, length=401)
> f.x <- cbind(psi = psi.weight(x), psp = psp.weight(x),
+ chi = chi.weight(x), rho = rho.weight(x))
> es <- expression(psi(x), {psi*minute}(x), chi(x), rho(x))
> leg <- as.expression(lapply(seq_along(es), function(i)
+ substitute(C == E, list(C=colnames(f.x)[i], E=es[[i]]))))
> matplot(x, f.x, type = "l", lwd = 1.5,
+ main = "psi.weight(.) etc -- 'optimal'")
> abline(h = 0, v = 0, lwd = 2, col = "#D3D3D380") # opaque gray
> legend("bottom", leg, inset = .01,
+ lty = 1:4, col = 1:4, lwd = 1.5, bg = "#FFFFFFC0")
>
>
>
> cleanEx()
> nameEx("woodmod.dat")
> ### * woodmod.dat
>
> flush(stderr()); flush(stdout())
>
> ### Name: woodmod.dat
> ### Title: Modified Wood Data
> ### Aliases: woodmod.dat
> ### Keywords: datasets
>
> ### ** Examples
>
> data(woodmod.dat)
> woodmod.dat
V1 V2 V3 V4 V5
1 0.573 0.1059 0.465 0.538 0.841
2 0.651 0.1356 0.527 0.545 0.887
3 0.606 0.1273 0.494 0.521 0.920
4 0.437 0.1591 0.446 0.423 0.992
5 0.547 0.1135 0.531 0.519 0.915
6 0.444 0.1628 0.429 0.411 0.984
7 0.489 0.1231 0.562 0.455 0.824
8 0.413 0.1673 0.418 0.430 0.978
9 0.536 0.1182 0.592 0.464 0.854
10 0.685 0.1564 0.631 0.564 0.914
11 0.664 0.1588 0.506 0.481 0.867
12 0.703 0.1335 0.519 0.484 0.812
13 0.653 0.1395 0.625 0.519 0.892
14 0.586 0.1114 0.505 0.565 0.889
15 0.534 0.1143 0.521 0.570 0.889
16 0.523 0.1320 0.505 0.612 0.919
17 0.580 0.1249 0.546 0.608 0.954
18 0.448 0.1028 0.522 0.534 0.918
19 0.417 0.1687 0.405 0.415 0.981
20 0.528 0.1057 0.424 0.566 0.909
>
> data(wood, package = "robustbase")
> stopifnot(data.matrix(woodmod.dat) ==
+ data.matrix(wood [,1:5]))
Error: data.matrix(woodmod.dat) == data.matrix(wood[, 1:5]) are not all TRUE
Execution halted
`predict` doesn't work for `glmRob()` with new data
predict doesn't work correctly for glmRob objects if a new dataset is supplied.
library(robust)
#> Loading required package: fit.models
data(breslow.dat)
bres.rob <- glmRob(sumY ~ Age10 + Base4 * Trt, family = poisson(),
data = breslow.dat)
predict(bres.rob, newdata = breslow.dat[1,])
#> Error in NextMethod("predict"): no method to invokeCreated on 2022-09-30 with reprex v2.0.2
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