Thank you for the help. My focus was to split data frame for a different
function, not lm. I could provide detail of that lengthy function instead I
provided the lm function.
The comment were very helpful.
Thanks;
NIL
On Mon, Aug 29, 2011 at 3:37 PM, Dimitris Rizopoulos <
d.rizopou...@erasmusmc
well, if a pooled estimate of the residual standard error is not
desirable, then you just need to set argument 'pool' of lmList() to
FALSE, e.g.,
mlis <- lmList(yvar ~ . - clvar | clvar, data = df, pool = FALSE)
summary(mlis)
Best,
Dimitris
On 8/29/2011 9:20 PM, Dennis Murphy wrote:
Hi:
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 subd
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 th
Hi:
This is straightforward to do with the plyr package:
# install.packages('plyr')
library('plyr')
set.seed(1234)
df <- data.frame(clvar = rep(1:4, each = 10), yvar = rnorm(40, 10, 6),
var1 = rnorm(40, 10, 4), var2 = rnorm(40, 10, 4),
var3 = rnorm(40, 5, 2), var
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,
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