103 lines
3.2 KiB
Plaintext
103 lines
3.2 KiB
Plaintext
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R Under development (unstable) (2022-07-22 r82614) -- "Unsuffered Consequences"
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Copyright (C) 2022 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu (64-bit)
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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> #
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> # This code verified some oddities about model frames
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> #
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> options(contrasts=c("contr.treatment", "contr.poly")) # clean slate
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>
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> tdata <- data.frame(y=1:10, x1=letters[c(1,2,3,1,2,3,1,2,3,1)],
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+ x2=LETTERS[c(1,2,3,4,1,2,3,4,1,2)],
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+ stringsAsFactors=TRUE)
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> tdata$x3 <- as.character(tdata$x1)
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>
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> fit1 <- lm(y ~ x1 + x2, tdata, x=TRUE)
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> m1 <- fit1$model
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> t1 <- terms(fit1)
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>
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> # Lesson 1: xlev is ignored when the variable already has levels
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> # as an attribute
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>
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> temp <- list(x1=c("a", "c", "b"), x2=LETTERS[4:1])
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> x2 <- model.matrix(t1, m1, xlev=temp)
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> x3 <- model.matrix(t1, tdata, xlev=temp)
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>
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> all.equal(x2, fit1$x)
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[1] TRUE
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> !is.logical(all.equal(x2, x3)) # x2 and x3 do not agree
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[1] TRUE
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> attributes(m1$x1)
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$levels
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[1] "a" "b" "c"
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$class
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[1] "factor"
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>
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>
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> # Lesson 2: character variables do not have their levels
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> # remembered as attributes in the model frame, but these are
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> # found in fit$xlevels and fit$contrasts.
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> # However, the xlev argument is still ignored for a model frame!
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> fit2 <- lm(y ~ x3 + x2, tdata, x=TRUE)
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> m2 <- fit2$model
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> x3 <- model.matrix(terms(fit2), m2, xlev=fit2$xlevels)
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> x4 <- model.matrix(terms(fit2), m2, xlev=list(x3=letters[3:1]))
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> x5 <- model.matrix(terms(fit2), tdata, xlev=list(x3=letters[3:1]))
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> all.equal(fit2$x, x3)
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[1] TRUE
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> all.equal(x3, x4)
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[1] TRUE
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> all.equal(x3, x5) # FALSE
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[1] "Attributes: < Component \"dimnames\": Component 2: 1 string mismatch >"
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[2] "Mean relative difference: 2.333333"
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>
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>
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> # Lesson 3: contrasts.arg is relevant, even when the model frame
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> # has a saved contrast
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> ctemp <- list(x1="contr.SAS", x2= contr.helmert(LETTERS[1:4]))
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> x4 <- model.matrix(t1, m1, contrasts.arg=ctemp) # no saved contrast
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>
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> fit3 <- lm(y ~ x1 + C(x2, contr.SAS), tdata)
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> m3 <- fit3$model
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> attr(m3[[3]], 'contr') # the contrast is saved
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A B C
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A 1 0 0
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B 0 1 0
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C 0 0 1
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D 0 0 0
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> c2 <- ctemp
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> names(c2) <- names(fit3$contrasts)
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> x5 <- model.matrix(terms(fit3), m3, contrasts.arg=c2)
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> all.equal(x4, x5, check.attributes=FALSE)
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[1] TRUE
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>
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> # Lesson 4: and this holds for a character variable as well
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> fit4 <- lm(y ~ x3 + C(x2, contr.SAS), tdata, x=TRUE)
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> c4 <- ctemp
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> names(c4) <- names(fit4$contrasts)
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> x6 <- model.matrix(terms(fit4), fit4$model, xlev=fit4$xlevels, contrasts.arg=c4)
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> x7 <- model.matrix(terms(fit4), fit4$model, contrasts.arg=c4)
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> all.equal(x7, x6)
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[1] TRUE
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> all.equal(x7, x5, check.attributes=FALSE)
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[1] TRUE
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>
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> proc.time()
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user system elapsed
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0.128 0.017 0.138
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