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
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--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
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