On Wed, Oct 1, 2008 at 7:21 AM, Cézar Freitas <[EMAIL PROTECTED]> wrote:
> Hi. I searched the list and didn't found nothing similar to this. I
> simplified my example like below:
>
> #I need calculate correlation (for example) between 2 columns classified by a
> third one at a data.frame, like below:
>
> #number of rows
> nr = 10
>
> #the third column is to enforce that I need correlation on two variables only
> dataf =
> as.data.frame(matrix(c(rnorm(nr),rnorm(nr)*2,runif(nr),sort(c(1,1,2,2,3,3,sample(1:3,nr-6,replace=TRUE)))),ncol=4))
> names(dataf)[4] = "class"
>
> #> dataf
> # V1 V2 V3 class
> #1 0.56933020 1.2529931 0.30774422 1
> #2 0.41702299 -1.6441547 0.76140046 1
> #3 -1.07671647 -4.8747575 0.43706944 1
> #4 -1.97701167 1.3015196 0.04390175 2
> #5 0.56501325 1.8597720 0.08174124 2
> #6 0.70068638 1.7922641 0.74730126 2
> #7 -1.39956177 -1.9918904 0.64521918 3
> #8 0.27086664 0.3745362 0.61026133 3
> #9 0.04282347 3.7360407 0.48696109 3
> #10 -0.34262654 0.7933674 0.09824913 3
>
> #I tried:
>
> tapply(dataf$V1, dataf$class, cor, dataf$V2)
> #Error FUN(X[[1L]], ...) : incompatible dimensions
>
> tapply(dataf$V1, dataf$class, cor, tapply(dataf$V2, dataf$class))
> #Error FUN(X[[1L]], ...) : incompatible dimensions
>
> #But using "by" I obtain:
>
> by(dataf[,c("V1","V2")], dataf$class, cor)
>
> #dataf$class: 1
> # V1 V2
> #V1 1.00000 0.91777
> #V2 0.91777 1.00000
> #--------------------------------------------------------------------------------------------------
> #dataf$class: 2
> # V1 V2
> #V1 1.000000 0.987857
> #V2 0.987857 1.000000
> #--------------------------------------------------------------------------------------------------
> #dataf$class: 3
> # V1 V2
> #V1 1.0000000 0.7318938
> #V2 0.7318938 1.0000000
>
> #My interest is on cor(V1,V2)[1,2], so I can take 0.91777, 0.987857 and
> 0.7318938, but I think that tapply can works better, if I can solve the
> problem.
You might want to have a look at the plyr package:
install.packages("plyr")
library(plyr)
# You can easily control the output data type:
# d = data.frame, a = array, l = list
ddply(dataf, .(class), function(df) data.frame(cor(df[, 1:2])))
daply(dataf, .(class), function(df) cor(df[, 1:2]))
dlply(dataf, .(class), function(df) cor(df[, 1:2]))
# Or for the minimal value you want
ddply(dataf, .(class), function(df) cor(df$V1, df$V2))
# Note that plyr preserves labels so it's easier to match up with the
original data
# Learn more at http://had.co.nz/plyr
Hadley
--
http://had.co.nz/
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