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## test handing of weights and offset argument
require(robustbase)
## generate simple example data (extension of the one in ./NAcoef.R )
data <- expand.grid(x1=letters[1:3], x2=LETTERS[1:4], rep=1:3,
KEEP.OUT.ATTRS = FALSE)
## generate offset column
data$os <- 1:nrow(data)
set.seed(1)
data$y <- data$os + rnorm(nrow(data))
## add collinear variables
data$x3 <- rnorm(nrow(data))
data$x4 <- rnorm(nrow(data))
data$x5 <- data$x3 + data$x4 ## lm() will have 'x5' "aliased" (and give coef = NA)
## add some NA terms
data$y[1] <- NA
data$x4[2:3] <- NA ## to test anova
## generate weights
## some obs with weight 0
data$weights <- as.numeric(with(data, x1 != 'c' | (x2 != 'B' & x2 != 'C')))
## some obs with weight 2
data$weights[data$x1 == 'b'] <- 2
## data2 := {data + weights}, encoded in "data2" (-> "ok" for coef(), not for SE)
data2 <- rbind(subset(data, weights > 0),
subset(data, weights == 2))
## using these parameters we're essentially forcing lmrob() to
## fit a classic model --> easier to compare to lm()
ctrl <- lmrob.control(psi="optimal", tuning.chi = 20, bb = 0.0003846154,
tuning.psi=20, method="SM", cov=".vcov.w")
## SM = MM == the case where .vcov.avar1 was also defined for
## Classical models start with 'cm', robust just with 'rm' (or just 'm'),
## "." - models drop 'x5' (which is aliased / extraneous by construction) :
(cm0 <- lm (y ~ x1*x2 + x3 + x4 + x5 + offset(os), data))
(cm0.<- lm (y ~ x1*x2 + x3 + x4 + offset(os), data))
(cm1 <- lm (y ~ x1*x2 + x3 + x4 + x5 + offset(os), data, weights=weights))
(cm1.<- lm (y ~ x1*x2 + x3 + x4 + offset(os), data, weights=weights))
(cm2 <- lm (y ~ x1*x2 + x3 + x4 + x5, data2, offset=os))
(cm2.<- lm (y ~ x1*x2 + x3 + x4, data2, offset=os))
(rm0 <- lmrob(y ~ x1*x2 + x3 + x4 + x5 + offset(os), data, control=ctrl))
(rm0.<- lmrob(y ~ x1*x2 + x3 + x4 + offset(os), data, control=ctrl))
set.seed(2)
(rm1 <- lmrob(y ~ x1*x2 + x3 + x4 + x5 + offset(os), data, weights=weights, control=ctrl))
set.seed(2)
(rm1.<- lmrob(y ~ x1*x2 + x3 + x4 + offset(os), data, weights=weights, control=ctrl))
set.seed(2)
(rm2 <- lmrob(y ~ x1*x2 + x3 + x4 + x5, data2, offset=os, control=ctrl))
set.seed(2)
(rm2.<- lmrob(y ~ x1*x2 + x3 + x4, data2, offset=os, control=ctrl))
sc0 <- summary(cm0)
sc1 <- summary(cm1)
sc2 <- summary(cm2)
(sr0 <- summary(rm0))
(sr1 <- summary(rm1))
(sr2 <- summary(rm2))
## test Estimates, Std. Errors, ...
nc <- names(coef(cm1))
nc. <- setdiff(nc, "x5") # those who are "valid"
stopifnot(exprs = {
all.equal(coef(cm0.),coef(cm0)[nc.])
all.equal(coef(cm1.),coef(cm1)[nc.])
all.equal(coef(cm2.),coef(cm2)[nc.])
all.equal(coef(cm1), coef(cm2))
all.equal(coef(rm1), coef(rm2))
all.equal(coef(sc0), coef(sr0))
all.equal(coef(sc1), coef(sr1))
all.equal(coef(sc2), coef(sr2))
})
## test class "lm" methods that do not depend on weights
meths1 <- c("family",
"formula",
"labels",
"model.matrix",
"na.action",
"terms")
for (meth in meths1)
stopifnot(all.equal(do.call(meth, list(rm0)),
do.call(meth, list(rm1))))
## class "lm" methods that depend on weights
## FIXME:
meths2 <- c(#"AIC",
"alias",
#"BIC",
"case.names",
"coef",
"confint",
#"cooks.distance",
#"deviance",
"df.residual",
#"dfbeta",
#"dfbetas",
#"drop1",
"dummy.coef",
#"effects",
#"extractAIC",
#"hatvalues",
#"influence",
"kappa",
#"logLik",
#"model.frame", ## disable because of zero.weights attribute
"nobs",
"predict",
#"proj",
#"rstandard",
#"rstudent",
#"simulate",
##"summary", ## see above
"variable.names",
##"vcov", ## see below
"weights")
op <- options(warn = 1)# print immediately
for (meth in meths2) {
cat(meth,":")
.SW. <- if(meth == "weights") suppressWarnings else identity # for suppressing
## No weights defined for this object. Use type="robustness" ....
stopifnot(all.equal(do.call(meth, list(cm1)),
do.call(meth, list(rm1))),
all.equal(do.call(meth, list(cm2)),
.SW.(do.call(meth, list(rm2)))))
cat("\n")
}
options(op)# reverting
## further tests:
anova(rm1, update(rm1, ~ . - x4 - x5))
anova(rm2, update(rm2, ~ . - x4 - x5))
stopifnot(all.equal(fitted(cm0), fitted(rm0)),
all.equal(fitted(cm1), fitted(rm1)),
all.equal(fitted(cm2), fitted(rm2)))
nd <- expand.grid(x1=letters[1:3], x2=LETTERS[1:4])
set.seed(3)
nd$x3 <- rnorm(nrow(nd))
nd$x4 <- rnorm(nrow(nd))
nd$x5 <- rnorm(nrow(nd)) # (*not* the sum x3+x4 !)
nd$os <- nrow(nd):1
wts <- runif(nrow(nd))
stopifnot(exprs = {
all.equal(predict(cm0, nd, interval="prediction"),
predict(rm0, nd, interval="prediction"))
all.equal(predict(cm1, nd, interval="prediction"),
predict(rm1, nd, interval="prediction"))
all.equal(predict(cm2, nd, interval="prediction"),
predict(rm2, nd, interval="prediction"))
all.equal(predict(cm0, nd, interval="prediction", weights=wts),
predict(rm0, nd, interval="prediction", weights=wts))
all.equal(predict(cm1, nd, interval="prediction", weights=wts),
predict(rm1, nd, interval="prediction", weights=wts))
all.equal(predict(cm2, nd, interval="prediction", weights=wts),
predict(rm2, nd, interval="prediction", weights=wts),
tolerance=1e-7)
})
## Padding can lead to differing values here
## so test only full rank part
qrEQ <- function(m1, m2) {
q1 <- qr(m1)
q2 <- qr(m2)
r <- 1:q1$rank
stopifnot(q1$rank == q2$rank,
all.equal(q1$pivot, q2$pivot),
all.equal(q1$qraux[r],q2$qraux[r]),
all.equal(q1$qr[r,r], q2$qr[r,r]))
}
qrEQ(cm0, rm0)
qrEQ(cm1, rm1)
qrEQ(cm2, rm2)
stopifnot(all.equal(residuals(cm0), residuals(rm0)),
all.equal(residuals(cm1), residuals(rm1)),
all.equal(residuals(cm2), residuals(rm2)),
all.equal(resid(cm0, type="pearson"), resid(rm0, type="pearson")),
all.equal(resid(cm1, type="pearson"), resid(rm1, type="pearson")),
all.equal(resid(cm2, type="pearson"), resid(rm2, type="pearson")))
## R 3.5.0: vcov(*, complete=TRUE) new default ==> same NA's as coef()
if(interactive()) withAutoprint({
op <- options(width = 130, digits = 2) # --> vcov() rows fit on 1 line
vcov(cm0) # 'x5' is NA
vcov(cm2) # 'x5', 'x1c:2B', 'x1c:2C' rows & columns are NA
options(op)
})
(no.C <- is.na(match("complete", names(formals(stats:::vcov.lm))))) ## temporary _FIXME_
vcovC <- if(no.C) function(M, ...) vcov(M, complete=FALSE, ...) else vcov # (complete=TRUE)
stopifnot(all.equal(vcov(cm0), vcovC(rm0), check.attributes=FALSE),
all.equal(vcov(cm1), vcovC(rm1), check.attributes=FALSE),
all.equal(vcov(cm2), vcovC(rm2), check.attributes=FALSE))
## "clean":
cln <- function(vc) structure(vc, weights=NULL, eigen=NULL)
## .vcov.avar1() is not recommended here, but also should work with singular / NA coef case:
ok0 <- !is.na(coef(rm0))
tools::assertWarning(verbose = TRUE, # on non-M1mac, there is a 2nd warning (not shown here!):
vr0.NA<- vcov(rm0, cov=".vcov.avar1", complete=NA))
vr0.T <- vcov(rm0, cov=".vcov.avar1", complete=TRUE)
vr0.F <- vcov(rm0, cov=".vcov.avar1", complete=FALSE)
stopifnot(identical(dim(vr0.NA), dim(vr0.T)),
identical(dim(vr0.F), dim(vr0.T) - 1L), dim(vr0.F) == 14,
all.equal(cln(vr0.F), vr0.T[ok0,ok0], tol = 1e-15))
if(!no.C) {
vc0.T <- vcov(cm0, complete=TRUE)
vc0.F <- vcov(cm0, complete=FALSE)
}
ok1 <- !is.na(coef(rm1))
## cannot work because init/fit residuals are not of full length
tools::assertError(vr1.NA<- vcov(rm1, cov=".vcov.avar1", complete=NA))
tools::assertError(vr1.T <- vcov(rm1, cov=".vcov.avar1", complete=TRUE ))
tools::assertError(vr1.F <- vcov(rm1, cov=".vcov.avar1", complete=FALSE))
## instead, must refit
rm1. <- update(rm1, control = within(ctrl, cov <- ".vcov.avar1"))
vr1.NA<- vcov(rm1., complete=NA)
vr1.T <- vcov(rm1., complete=TRUE)
vr1.F <- vcov(rm1., complete=FALSE)
stopifnot(identical(vr1.F, vr1.NA), # in this case
identical(dim(vr1.F), dim(vr1.T) - 3L), dim(vr1.F) == 12, isSymmetric(vr1.T),
identical(rownames(vr1.F), rownames(vr1.T)[ok1]),
all.equal(cln(vr1.F), vr1.T[ok1,ok1], tol=1e-15))
if(FALSE) ## ERROR "exact singular" (probably *NOT* to fix, as TRUE/FALSE do work !)
vr2.NA<- vcov(rm2, cov=".vcov.avar1", complete=NA) # "almost singular" warning
vr2.T <- vcov(rm2, cov=".vcov.avar1", complete=TRUE)
vr2.F <- vcov(rm2, cov=".vcov.avar1", complete=FALSE)
stopifnot(TRUE, # identical(dim(vr2.NA), dim(vr2.T)),
identical(dim(vr2.F), dim(vr2.T) - 3L), dim(vr2.F) == 12,
identical(rownames(vr2.F), rownames(vr1.F)),
identical(rownames(vr2.T), rownames(vr1.T)),
all.equal(cln(vr2.F), vr2.T[ok1,ok1], tol=1e-15))
## Hmm, the supposedly heteroscedastic-robust estimates *are* very different:
all.equal(vcov(cm0), vcovC(rm0, cov = ".vcov.avar1"), check.attributes=FALSE) # rel.diff. 0.5367564
if(FALSE) # does not make sense
all.equal(vcov(cm1), vcovC(rm1, cov = ".vcov.avar1"), check.attributes=FALSE)
all.equal(vcov(cm2), vcovC(rm2, cov = ".vcov.avar1"), check.attributes=FALSE) # rel.diff. 0.5757642
## Null fits (rank(X)==0) are tested in NAcoef.R
## testing weight=0 bug
lmrob(y ~ x3, data, weights=weights)
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