[R] Getting rid of for loops

[Kevin J Emerson]

Hello R-users!

I have a style question.  I know that for loops are somewhat frowned upon in
R, and I was trying to figure out a nice way to do something without using
loops, but figured that i could get it done quickly using them.  I am now
looking to see what kind of tricks I can use to make this code a bit more
aesthetically appealing to other R users (and learn something about R along
the way...).

Here's the problem.  I have a data.frame with 4 columns of dependent
variables and then ~35 columns of predictor variables (factors) [for those
interested, it is a qtl problem, where the predictors are genotypes at DNA
markers and the dependent variable is a biological trait].  I want to go
through all pairwise combinations of predictor variables and perform an
anova with two predictors and their interaction on a given dependent
variable.  I then want to store the p.value of the interaction term, along
with the predictor variable information.  So I want to end up with a
dataframe at the end with the two variable names and the interaction p value
in each row, for all pairwise combinations of predictors.  I used the
following code:

# qtl is the original data.frame, and my dependent var in this case is
# qtl$CPP.

marker1 <- NULL
marker2 <- NULL
p.interaction <- NULL
for ( i in 5:40) {   # cols 5 - 41 are the predictor factors
	for (j in (i+1):41) {
		marker1 <- rbind(marker1,names(qtl)[i])
		marker2 <- rbind(marker2,names(qtl)[j])
		tmp2 <- summary(aov(tmp$CPP ~ tmp[,i] * tmp[,j]))[[1]]
		p.interaction <- rbind(p.interaction, tmp2$"Pr(>F)"[3])
	}
}

I have two questions:
(1) is there a nicer way to do this without having to invoke for loops?
(2) my other dependent variables are categorical in nature.  I need
basically the same information - I am looking for information regarding the
interaction of predictors on a categorical variable.  Any ideas on what
tests to use? (I am new to analysis of all-categorical data).

Thanks in advance!
Kevin

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Kevin Emerson
Center for Ecology and Evolutionary Biology
1210 University of Oregon
Eugene, OR 97403
USA
kemerson at uoregon.edu