[R] nested ANCOVA: still confused

Jeffrey Stratford stratja at auburn.edu
Thu Jan 26 00:56:31 CET 2006

Harold, Kingsford, and R-users,

I settled on using the lmer function.  I think the memory issue was more
a function of my poor coding than an actual memory problem.  I also
switched the label from "box" to "clutch" to avoid any potential
confusion with other functions. 

This coding seems to have worked:

> eabl <- lmer(rtot ~ sexv + (purban2|clutch), maxIter=1000, data=bb,

However, I have two remaining questions: (1)how concerned should I be
with the warning message below and (2) is there a way to invoke output
to get an estimate of the effect of purban2 (the proportion of urban
cover 200 m around a box) on feather color (rtot) and if there is a
difference between the sexes?   I used the summary function and it
doesn't tell me much (see output below). 

I'll read up mixed models when Pinheiro arrives but any suggestions for
diagnostics?  I'm going to repeat this study and expand it by doubling
or tripling the number of birds.  

Warning message:
nlminb returned message false convergence (8) 
 in: "LMEoptimize<-"(`*tmp*`, value = list(maxIter = 200, tolerance =

> summary(eabl)
Linear mixed-effects model fit by REML
Formula: rtot ~ sexv + (purban2 | clutch) 
   Data: bb 
      AIC      BIC    logLik MLdeviance REMLdeviance
 5164.284 6997.864 -2052.142   4128.792     4104.284
Random effects:
 Groups   Name               Variance  Std.Dev. Corr                    
 clutch   (Intercept)           502829   709.10                         
          purban20             1341990  1158.44 -0.477                  
          purban20.006711409   5683957  2384.10 -0.226  0.082           
          purban20.01342282    1772922  1331.51 -0.386  0.176  0.067  
# of obs: 235, groups: clutch, 74

Fixed effects:
            Estimate Std. Error t value
(Intercept)  5950.01     241.59  24.628
sexvm        1509.07     145.73  10.355

Correlation of Fixed Effects:
sexvm -0.304

Thanks many time over,


Jeffrey A. Stratford, Ph.D.
Postdoctoral Associate
331 Funchess Hall
Department of Biological Sciences
Auburn University
Auburn, AL 36849
FAX 334-844-9234
>>> "Doran, Harold" <HDoran at air.org> 01/25/06 6:37 AM >>>
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,

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,

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


-----Original Message-----
From:	Jeffrey Stratford [mailto:stratja at auburn.edu]
Sent:	Tue 1/24/2006 8:57 PM
To:	Doran, Harold; r-help at stat.math.ethz.ch
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 

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,


Jeffrey A. Stratford, Ph.D.
Postdoctoral Associate
331 Funchess Hall
Department of Biological Sciences
Auburn University
Auburn, AL 36849
FAX 334-844-9234
>>> "Doran, Harold" <HDoran at 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,

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

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,

Hope this helps.

-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Jeffrey Stratford
Sent: Tuesday, January 24, 2006 11:34 AM
To: r-help at 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,

but this is not working.

Any suggestions would be greatly appreciated.



Jeffrey A. Stratford, Ph.D.
Postdoctoral Associate
331 Funchess Hall
Department of Biological Sciences
Auburn University
Auburn, AL 36849
FAX 334-844-9234

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