R Under development (unstable) (2023-08-12 r84939 ucrt) -- "Unsuffered Consequences" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 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. > # > # This code verified some oddities about model frames > # > options(contrasts=c("contr.treatment", "contr.poly")) # clean slate > > tdata <- data.frame(y=1:10, x1=letters[c(1,2,3,1,2,3,1,2,3,1)], + x2=LETTERS[c(1,2,3,4,1,2,3,4,1,2)], + stringsAsFactors=TRUE) > tdata$x3 <- as.character(tdata$x1) > > fit1 <- lm(y ~ x1 + x2, tdata, x=TRUE) > m1 <- fit1$model > t1 <- terms(fit1) > > # Lesson 1: xlev is ignored when the variable already has levels > # as an attribute > > temp <- list(x1=c("a", "c", "b"), x2=LETTERS[4:1]) > x2 <- model.matrix(t1, m1, xlev=temp) > x3 <- model.matrix(t1, tdata, xlev=temp) > > all.equal(x2, fit1$x) [1] TRUE > !is.logical(all.equal(x2, x3)) # x2 and x3 do not agree [1] TRUE > attributes(m1$x1) $levels [1] "a" "b" "c" $class [1] "factor" > > > # Lesson 2: character variables do not have their levels > # remembered as attributes in the model frame, but these are > # found in fit$xlevels and fit$contrasts. > # However, the xlev argument is still ignored for a model frame! > fit2 <- lm(y ~ x3 + x2, tdata, x=TRUE) > m2 <- fit2$model > x3 <- model.matrix(terms(fit2), m2, xlev=fit2$xlevels) > x4 <- model.matrix(terms(fit2), m2, xlev=list(x3=letters[3:1])) > x5 <- model.matrix(terms(fit2), tdata, xlev=list(x3=letters[3:1])) > all.equal(fit2$x, x3) [1] TRUE > all.equal(x3, x4) [1] TRUE > all.equal(x3, x5) # FALSE [1] "Attributes: < Component \"dimnames\": Component 2: 1 string mismatch >" [2] "Mean relative difference: 2.333333" > > > # Lesson 3: contrasts.arg is relevant, even when the model frame > # has a saved contrast > ctemp <- list(x1="contr.SAS", x2= contr.helmert(LETTERS[1:4])) > x4 <- model.matrix(t1, m1, contrasts.arg=ctemp) # no saved contrast > > fit3 <- lm(y ~ x1 + C(x2, contr.SAS), tdata) > m3 <- fit3$model > attr(m3[[3]], 'contr') # the contrast is saved A B C A 1 0 0 B 0 1 0 C 0 0 1 D 0 0 0 > c2 <- ctemp > names(c2) <- names(fit3$contrasts) > x5 <- model.matrix(terms(fit3), m3, contrasts.arg=c2) > all.equal(x4, x5, check.attributes=FALSE) [1] TRUE > > # Lesson 4: and this holds for a character variable as well > fit4 <- lm(y ~ x3 + C(x2, contr.SAS), tdata, x=TRUE) > c4 <- ctemp > names(c4) <- names(fit4$contrasts) > x6 <- model.matrix(terms(fit4), fit4$model, xlev=fit4$xlevels, contrasts.arg=c4) > x7 <- model.matrix(terms(fit4), fit4$model, contrasts.arg=c4) > all.equal(x7, x6) [1] TRUE > all.equal(x7, x5, check.attributes=FALSE) [1] TRUE > > proc.time() user system elapsed 0.14 0.03 0.15