Displaying 4 results from an estimated 4 matches for "logdens".
2006 Jan 03
3
Package for multiple membership model?
Hello all:
I am interested in computing what the multilevel modeling literature calls a multiple membership model. More specifically, I am working with a data set involving clients and providers. The clients are the lower-level units who are nested within providers (higher-level). However, this is not nesting in the usual sense, as clients can belong to multple providers, which I understand
2003 May 12
1
update.lme trouble (PR#2985)
Try this
data(Assay)
as1 <- lme(logDens~sample*dilut, data=Assay,
random=pdBlocked(list(
pdIdent(~1),
pdIdent(~sample-1),
pdIdent(~dilut-1))))
update(as1,random=pdCompSymm(~sample-1))
update(as1,random=pdCompSymm(~sample-1))
update(as1,random=pdCompSymm(~sample-1)...
2006 Feb 07
0
lme and Assay data: Test for block effect when block is systematic - anova/summary goes wrong
Consider the Assay data where block, sample within block and dilut within block is random.
This model can be fitted with (where I define Assay2 to get an ordinary data frame rather
than a grouped data object):
Assay2 <- as.data.frame(Assay)
fm2<-lme(logDens~sample*dilut, data=Assay2,
random=list(Block = pdBlocked(list(pdIdent(~1), pdIdent(~sample-1),pdIdent(~dilut-1))) ))
Now, block has only 2 levels so I prefer to treat it as fixed:
fm3<-lme(logDens~Block+sample*dilut, data=Assay2,
random=list(Block = pdBlocked(list(pdIdent(~sample-1),pdId...
2003 Oct 04
2
mixed effects with nlme
Dear R users:
I have some difficulties analizing data with mixed effects NLME and the
last version of R. More concretely, I have a repeated measures design with
a single group and 2 experimental factors (say A and B) and my interest is
to compare additive and nonadditive models.
suj rv A B
1 s1 4 a1 b1
2 s1 5 a1 b2
3 s1 7 a1 b3
4 s1 1 a2