model.matrix() with na.action=NULL? - r

Question

I have a formula and a data frame, and I want to extract the model.matrix(). However, I need the resulting matrix to include the NAs that were found in the original dataset. If I were to use model.frame() to do this, I would simply pass it na.action=NULL. However, the output I need is of the model.matrix() format. Specifically, I need only the right-hand side variables, I need the output to be a matrix (not a data frame), and I need factors to be converted to a series of dummy variables.
I'm sure I could hack something together using loops or something, but I was wondering if anyone could suggest a cleaner and more efficient workaround. Thanks a lot for your time!
And here's an example:
dat <- data.frame(matrix(rnorm(20),5,4), gl(5,2))
dat[3,5] <- NA
names(dat) <- c(letters[1:4], 'fact')
ff <- a ~ b + fact
# This omits the row with a missing observation on the factor
model.matrix(ff, dat)
# This keeps the NA, but it gives me a data frame and does not dichotomize the factor
model.frame(ff, dat, na.action=NULL)
Here is what I would like to obtain:
(Intercept) b fact2 fact3 fact4 fact5
1 1 0.7266086 0 0 0 0
2 1 -0.6088697 0 0 0 0
3 NA 0.4643360 NA NA NA NA
4 1 -1.1666248 1 0 0 0
5 1 -0.7577394 0 1 0 0
6 1 0.7266086 0 1 0 0
7 1 -0.6088697 0 0 1 0
8 1 0.4643360 0 0 1 0
9 1 -1.1666248 0 0 0 1
10 1 -0.7577394 0 0 0 1

Answer 1

Joris's suggestion works, but a quicker and cleaner way to do this is via the global na.action setting. The 'Pass' option achieves our goal of preserving NA's from the original dataset.
Option 1: Pass
Resulting matrix will contain NA's in rows corresponding to the original dataset.
options(na.action='na.pass')
model.matrix(ff, dat)
Option 2: Omit
Resulting matrix will skip rows containing NA's.
options(na.action='na.omit')
model.matrix(ff, dat)
Option 3: Fail
An error will occur if the original data contains NA's.
options(na.action='na.fail')
model.matrix(ff, dat)
Of course, always be careful when changing global options because they can alter behavior of other parts of your code. A cautious person might store the original setting with something like current.na.action <- options('na.action'), and then change it back after making the model.matrix.

Answer 2

Another way is to use the model.frame function with argument na.action=na.pass as your second argument to model.matrix:
> model.matrix(ff, model.frame(~ ., dat, na.action=na.pass))
(Intercept) b fact2 fact3 fact4 fact5
1 1 -1.3560754 0 0 0 0
2 1 2.5476965 0 0 0 0
3 1 0.4635628 NA NA NA NA
4 1 -0.2871379 1 0 0 0
5 1 2.2684958 0 1 0 0
6 1 -1.3560754 0 1 0 0
7 1 2.5476965 0 0 1 0
8 1 0.4635628 0 0 1 0
9 1 -0.2871379 0 0 0 1
10 1 2.2684958 0 0 0 1
model.frame allows you to set the appropriate action for na.action which is maintained when model.matrix is called.

Answer 3

I half-stumbled across a simpler solution after looking at mattdevlin and Nathan Gould's answers:
model.matrix.lm(ff, dat, na.action = "na.pass")
model.matrix.default may not support the na.action argument, but model.matrix.lm does!
(I found model.matrix.lm from Rstudio's auto-complete suggestions — it appears to be the only non-default method for model.matrix if you haven't loaded any libraries that add others. Then I just guessed it might support the na.action argument.)

Answer 4

You can mess around a little with the model.matrix object, based on the rownames :
MM <- model.matrix(ff,dat)
MM <- MM[match(rownames(dat),rownames(MM)),]
MM[,"b"] <- dat$b
rownames(MM) <- rownames(dat)
which gives :
> MM
(Intercept) b fact2 fact3 fact4 fact5
1 1 0.9583010 0 0 0 0
2 1 0.3266986 0 0 0 0
3 NA 1.4992358 NA NA NA NA
4 1 1.2867461 1 0 0 0
5 1 0.5024700 0 1 0 0
6 1 0.9583010 0 1 0 0
7 1 0.3266986 0 0 1 0
8 1 1.4992358 0 0 1 0
9 1 1.2867461 0 0 0 1
10 1 0.5024700 0 0 0 1
Alternatively, you can use contrasts() to do the work for you. Constructing the matrix by hand would be :
cont <- contrasts(dat$fact)[as.numeric(dat$fact),]
colnames(cont) <- paste("fact",colnames(cont),sep="")
out <- cbind(1,dat$b,cont)
out[is.na(dat$fact),1] <- NA
colnames(out)[1:2]<- c("Intercept","b")
rownames(out) <- rownames(dat)
which gives :
> out
Intercept b fact2 fact3 fact4 fact5
1 1 0.2534288 0 0 0 0
2 1 0.2697760 0 0 0 0
3 NA -0.8236879 NA NA NA NA
4 1 -0.6053445 1 0 0 0
5 1 0.4608907 0 1 0 0
6 1 0.2534288 0 1 0 0
7 1 0.2697760 0 0 1 0
8 1 -0.8236879 0 0 1 0
9 1 -0.6053445 0 0 0 1
10 1 0.4608907 0 0 0 1
In any case, both methods can be incorporated in a function that can deal with more complex formulae. I leave the exercise to the reader (what do I loath that sentence when I meet it in a paper ;-) )