R version 4.4.0 RC (2024-04-16 r86458 ucrt) -- "Puppy Cup"
Copyright (C) 2024 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 
   0.17    0.03    0.18