GLMM in R

Le vendredi 31 mai 2013 ? 22:27 -0300, Luis Fernando Garc?a Hern?ndez a
?crit :
Dear Friends,

I am new on R so I ask you to excuse me if this question sounds fool. I
want to see if there is a significativa relationship between the mating
(response variable) and several explanatory variables such as individual
number (categorical), leg movemente (continous) and the reuse of
individuals (categorical). My data looks like this

MatingIDReplicationShaking1M1R1100M1R2140M2R1150M2R2121M3R1140M3R2171M4R1190
M4R2221M5R1180M5R2161M6R117

1 means mating happened, 0 means did not occur.

I am trying to organize the data to apply a GLMM to the data, but have not
 been able to do it.I followed the model proposed by CRawley for binary
response with pseudorreplication but does not wok. The script goes like this

model2<-lmer(Mating~Replication+(1 ID),family=binomial,method=PQL)

Can somebody tell me what part is wrong and how could I fix it? If you know
a better method, will be really welcomed!
You do not tell us what you mean by "wrong", but at least in the form
above it will not work. Try with (1|ID), and use "REML" and "nAGQ"
arguments instead of the deprecated "method".

Regards
All the best!

	[[alternative HTML version deleted]]

______________________________________________
R-help at 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.
Milan Bouchet-Valat <nalimilan <at> club.fr> writes:
Le vendredi 31 mai 2013 ? 22:27 -0300, Luis Fernando Garc?a Hern?ndez a
?crit :
Dear Friends,

I am new on R so I ask you to excuse me if this question sounds fool. I
want to see if there is a significativa relationship between the mating
(response variable) and several explanatory variables such as individual
number (categorical), leg movemente (continous) and the reuse of
individuals (categorical). My data looks like this

[snip]
MatingIDReplicationShaking1M1R1100M1R2140M2R1150M2R
2121M3R1140M3R2171M4R1190
M4R2221M5R1180M5R2161M6R117
Your data seem to have gotten mangled in pasting (the mailing
list doesn't take HTML-format postings), so I'm
not *quite* sure what's going on here ...

1 means mating happened, 0 means did not occur.

I am trying to organize the data to apply a GLMM to
 the data, but have not
 been able to do it.I followed the model proposed by CRawley 
for binary
response with pseudorreplication but does not wok. The script goes like this

model2<-lmer(Mating~Replication+(1 ID),family=binomial,method=PQL)
As pointed out, there is at least one thing wrong here, and it
essentially has to do with following a printed/dead-tree representation
when lmer is a fairly rapidly moving target.

  As well as being obsolete, the method argument should have been
a string ("PQL") in any case.

  I would suggest

glmer(Mating~...+(1|ID), family=binomial,data=...)

(I strongly recommend the use of a 'data' argument to pass
the model variables rather than pulling them from the workspace.

  Further questions should probably go to
r-sig-mixed-models at r-project.org
Can somebody tell me what part is wrong and how could
I fix it? If you know
a better method, will be really welcomed!
You do not tell us what you mean by "wrong", but at least in the form
above it will not work. Try with (1|ID), and use "REML" and "nAGQ"
arguments instead of the deprecated "method".