From: Adrian DUSA <adi_at_roda.ro>

Date: Tue 18 Jul 2006 - 01:40:30 EST

Date: Tue 18 Jul 2006 - 01:40:30 EST

Regarding your first question, try this:

On Monday 17 July 2006 05:18, Kevin J Emerson wrote:

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

-- Adrian DUSA Romanian Social Data Archive 1, Schitu Magureanu Bd 050025 Bucharest sector 5 Romania Tel./Fax: +40 21 3126618 \ +40 21 3120210 / int.101 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Tue Jul 18 01:44:28 2006

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