OK, we're getting somewhere. First, it looks as though (by the error message) that you have a big dataset. My first recommendation is to use lmer instead of lme, you will see a significant benefit in terms of computional speed.

For the model this would be

lmer(rtot ~ sexv +(purban|box:chick) + (purban|box), bb, na.action=na.omit)

Now, you have run out of memory. I don't know what operating system you are using, so go and see the appropriate FAQ for increasing memory for your OS. 

Second, I made a mistake in my reply. Your random statement should be random=~purban|box/chick denoting that chicks are nested in boxes, not boxes nested in chicks, sorry about that.

Now, why is it that each chick within box has the same value for purban? If this is so, why are you fitting that as a random effect? It seems not to vary across individual chicks, right? It seems there is only an effect of box and not an effect for chicks. Why not just fit a random effect only for box such as:

rtot.lme <- lme(fixed=rtot~sexv, random=~purban2|box, na.action=na.omit,bb)

or in lmer
lmer(rtot ~ sexv + (purban|box), bb, na.action=na.omit)

Harold
 


-----Original Message-----
From:	Jeffrey Stratford [mailto:stratja@auburn.edu]
Sent:	Tue 1/24/2006 8:57 PM
To:	Doran, Harold; r-help@stat.math.ethz.ch
Cc:	
Subject:	RE: [R] nested ANCOVA: still confused

R-users and Harold.

First, thanks for the advice;  I'm almost there.  

The code I'm using now is 

library(nlme)
bb <- read.csv("E:\\eabl_feather04.csv", header=TRUE)
bb$sexv <- factor(bb$sexv)
rtot.lme <- lme(fixed=rtot~sexv, random=~purban2|chick/box,
na.action=na.omit, data=bb)

A sample of the data looks like this 

box	chick	rtot	purban2	sexv
1	1	6333.51	0.026846	f
1	2	8710.884	0.026846	m
2	1	5810.007	0.161074	f
2	2	5524.33	0.161074	f
2	3	4824.474	0.161074	f
2	4	5617.641	0.161074	f
2	5	6761.724	0.161074	f
4	1	7569.673	0.208054	m
4	2	7877.081	0.208054	m
4	4	7455.55	0.208054	f
7	1	5408.287	0.436242	m
10	1	6991.727	0.14094	f
12	1	8590.207	0.134228	f
12	2	7536.747	0.134228	m
12	3	5145.342	0.134228	m
12	4	6853.628	0.134228	f
15	1	8048.717	0.033557	m
15	2	7062.196	0.033557	m
15	3	8165.953	0.033557	m
15	4	8348.58	0.033557	m
16	2	6534.775	0.751678	m
16	3	7468.827	0.751678	m
16	4	5907.338	0.751678	f
21	1	7761.983	0.221477	m
21	2	6634.115	0.221477	m
21	3	6982.923	0.221477	m
21	4	7464.075	0.221477	m
22	1	6756.733	0.281879	f
23	2	8231.496	0.134228	m

The error I'm getting is

Error in logLik.lmeStructInt(lmeSt, lmePars) : 
        Calloc could not allocate (590465568 of 8) memory
In addition: Warning messages:
1: Fewer observations than random effects in all level 2 groups in:
lme.formula(fixed = rtot ~ sexv, random = ~purban2 | chick/box,  
2: Reached total allocation of 382Mb: see help(memory.size) 

There's nothing "special" about chick 1, 2, etc.  These were simply the
order of the birds measured in each box so chick 1 in box 1 has nothing
to do with chick 1 in box 2.

Many thanks,

Jeff 

****************************************
Jeffrey A. Stratford, Ph.D.
Postdoctoral Associate
331 Funchess Hall
Department of Biological Sciences
Auburn University
Auburn, AL 36849
334-329-9198
FAX 334-844-9234
http://www.auburn.edu/~stratja
****************************************
>>> "Doran, Harold" <HDoran@air.org> 01/24/06 2:04 PM >>>
Dear Jeff:

I see the issues in your code and have provided what I think will solve
your problem. It is often much easier to get help on this list when you
provide a small bit of data that can be replicated and you state what
the error messages are that you are receiving. OK, with that said, here
is what I see. First, you do not need to use the syntax bb$sex in your
model, this can be significantly simplified. Second, you do not have a
random statement in your model.

Here is your original model:
lme(bb$rtot~bb$sex, bb$purban|bb$chick/bb$box, na.action=na.omit)

Here is what it should be:

lme(fixed = rtot~sex, random=~purban|chick/box, na.action=na.omit,
data=bb)

Notice there is a fixed and random call. You can simplify this as

lme(rtot~sex, random=~purban|chick/box, na.action=na.omit, bb)

Note, you can eliminate the "fixed=" portion but not the random
statement.

Last, if you want to do this in lmer, the newer function for mixed
models in the Matrix package, you would do

lmer(rtot~sex + (purban|box:chick) + (purban|box), na.action=na.omit,
data=bb)

Hope this helps.
Harold




-----Original Message-----
From: r-help-bounces@stat.math.ethz.ch
[mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Jeffrey Stratford
Sent: Tuesday, January 24, 2006 11:34 AM
To: r-help@stat.math.ethz.ch
Subject: [R] nested ANCOVA: still confused

Dear R-users,

I did some more research and I'm still not sure how to set up an ANCOVA
with nestedness.  Specifically I'm not sure how to express chicks nested
within boxes.  I will be getting Pinheiro & Bates (Mixed Effects Models
in S and S-Plus) but it will not arrive for another two weeks from our
interlibrary loan.

The goal is to determine if there are urbanization (purban) effects on
chick health (rtot) and if there are differences between sexes (sex) and
the effect of being in the same clutch (box).

The model is rtot = sex + purban + (chick)box.

I've loaded the package lme4.  And the code I have so far is

bb <- read.csv("C:\\eabl\\eabl_feather04.csv", header=TRUE) bb$sex <-
factor(bb$sex) rtot.lme <- lme(bb$rtot~bb$sex,
bb$purban|bb$chick/bb$box,
na.action=na.omit)

but this is not working.

Any suggestions would be greatly appreciated.

Thanks,

Jeff








****************************************
Jeffrey A. Stratford, Ph.D.
Postdoctoral Associate
331 Funchess Hall
Department of Biological Sciences
Auburn University
Auburn, AL 36849
334-329-9198
FAX 334-844-9234
http://www.auburn.edu/~stratja

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