mlis <- lmList(yvar ~ . - clvar | clvar, data = df, pool = FALSE) summary(mlis)
Best, Dimitris On 8/29/2011 9:20 PM, Dennis Murphy wrote:
Hi: Dimitris' solution is appropriate, but it needs to be mentioned that the approach I offered earlier in this thread differs from the lmList() approach. lmList() uses a pooled measure of error MSE (which you can see at the bottom of the output from summary(mlis) ), whereas the plyr approach subdivides the data into distinct sub-data frames and analyzes them as separate entities. As a result, the residual MSEs will differ between the two approaches, which in turn affects the significance tests on the model coefficients. You need to decide which approach is better for your purposes. Cheers, Dennis On Mon, Aug 29, 2011 at 12:02 PM, Dimitris Rizopoulos <d.rizopou...@erasmusmc.nl> wrote:You can do this using function lmList() from package nlme, without having to split the data frames, e.g., library(nlme) mlis<- lmList(yvar ~ . - clvar | clvar, data = df) mlis summary(mlis) I hope it helps. Best, Dimitris On 8/29/2011 5:37 PM, Nilaya Sharma wrote:Dear All Sorry for this simple question, I could not solve it by spending days. My data looks like this: # data set.seed(1234) clvar<- c( rep(1, 10), rep(2, 10), rep(3, 10), rep(4, 10)) # I have 100 level for this factor var; yvar<- rnorm(40, 10,6); var1<- rnorm(40, 10,4); var2<- rnorm(40, 10,4); var3<- rnorm(40, 5, 2); var4<- rnorm(40, 10, 3); var5<- rnorm(40, 15, 8) # just example df<- data.frame(clvar, yvar, var1, var2, var3, var4, var5) # manual splitting df1<- subset(df, clvar == 1) df2<- subset(df, clvar == 2) df3<- subset(df, clvar == 3) df4<- subset(df, clvar == 4) df5<- subset(df, clvar == 5) # i tried to mechanize it * for(i in 1:5) { df[i]<- subset(df, clvar == i) } I know it should not work as df[i] is single variable, do it did. But I could not find away to output multiple dataframes from this loop. My limited R knowledge, did not help at all ! * # working on each of variable, just trying simple function a<- 3:8 out1<- lapply(1:5, function(ind){ lm(df1$yvar ~ df1[, a[ind]]) }) p1<- lapply(out1, function(m)summary(m)$coefficients[,4][2]) p1<- do.call(rbind, p1) My ultimate objective is to apply this function to all the dataframes created (i.e. df1, df2, df3, df4, df5) and create five corresponding p-value vectors (p1, p2, p3, p4, p5). Then output would be a matrix of clvar and correponding p values clvar var1 var2 var3 var4 var5 1 2 3 4 Please help me ! Thanks NIL [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.-- Dimitris Rizopoulos Assistant Professor Department of Biostatistics Erasmus University Medical Center Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands Tel: +31/(0)10/7043478 Fax: +31/(0)10/7043014 Web: http://www.erasmusmc.nl/biostatistiek/ ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
-- Dimitris Rizopoulos Assistant Professor Department of Biostatistics Erasmus University Medical Center Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands Tel: +31/(0)10/7043478 Fax: +31/(0)10/7043014 Web: http://www.erasmusmc.nl/biostatistiek/ ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
