CODEWITHSUNDEEP
rplyr
tomw
tomw

Reputation: 3158

Pass glm predictors from a list

I have a large set of model specifications to test, which share a dv but have unique IVs. In the following example

foo <- data.frame(dv  = sample(c(0,1), 100, replace=T),
                  x1 = runif(100),
                  x2 = runif(100))

I want the first model to only include x1, the second x2, the third both, and the fourth their interaction. So I thought a sensible way would be to build a list of formula statements:

bar <- list("x1",
            "x2", 
            "x1+x2",
            "x1*x2")

which I would then use in a llply call from the plyr package to obtain a list of model objects. 

require(plyr)
res <- llply(bar, function(i) glm(dv ~ i, data = foo, family = binomial()))

Unfortunately I'm told

Error in model.frame.default(formula = dv ~ i, data = foo, drop.unused.levels = TRUE):variable lengths differ (found for 'i')

Obviously I'm mixing up something fundamental--do I need to manipulate the original foo list in some fashion?

Upvotes: 0

Views: 870

Answers (2)

Andy
Andy

Reputation: 4659

Your problem is with how you are specifying the formula, since inside the function i is a variable. This would work:

glm(paste("dv ~", i), data = foo, family = binomial())

Upvotes: 3

joran
joran

Reputation: 173537

The problem is that dv ~ i isn't a formula. i is (inside the anonymous function) simply a symbol that represents a variable containing a character value.

Try this:

bar <- list("dv~x1",
            "dv~x2", 
            "dv~x1+x2",
            "dv~x1*x2")

res <- llply(bar, function(i) glm(i, data = foo, family = binomial()))

But setting statistical issues aside, it might possibly be easier to use something like ?step or ?stepAIC in the MASS package for tasks similar to this?

Upvotes: 2

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