[R-sig-ME] Difficulty with mixed models in R

[ONKELINX, Thierry]

Dear Michael,

I think you want 

model <- lme(PDD ~ Status + MD,
             data = Penguin,
             random = ~ Status | Penguin/Ntrip/Ndive,
             correlation = corAR1()) 

And make shure that Ndive is unique for each dive. You'll get into
trouble if each Ntrip has Ndive = 1.

If you don't have multiple observations per dive than you don't need to
add Ndive.
model <- lme(PDD ~ Status + MD,
             data = Penguin,
             random= ~ Status | Penguin/Ntrip,
             correlation = corAR1()) 

HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens Michael
Beaulieu
Verzonden: dinsdag 21 oktober 2008 16:21
Aan: r-sig-mixed-models at r-project.org
Onderwerp: [R-sig-ME] Difficulty with mixed models in R

Dear R users,

I would like two compare the diving behaviour of two groups of penguins 
(7 penguin in each group). Each penguin performed several dives within 
several foraging trips. As a result, I got a huge data set of dives 
(nearly 100000).
To compare the diving behaviour (in the script PDD) of the two groups, I

used a mixed model with:
-the penguin as a random factor,
-the number of dives nested in the foraging trip as a repeated factor,
-the group, the foraging trip and maximal depth (MD) as fixed factors.
Covariance structure was auto-regressive.

Here is the script I used:

library(nlme)
penguin = read.table(file = "C://Documents and 
Settings//Administrateur//Bureau//mk9ter.txt", header = T, sep = "\t", 
dec = ".", na.strings="NA")
penguin$Penguin =  as.factor(penguin$Penguin)
penguin$Ndive =  as.factor(penguin$Ndive)
penguin$Ntrip =  as.factor(penguin$Ntrip)
penguin = penguin[!is.na(penguin$Penguin),]
Penguin = groupedData(formula = PDD ~ Ntrip | Penguin, data = penguin)
rm(penguin)
model = lme(PDD ~ Ntrip + Status + MD + Ntrip/Ndive,
             data=Penguin,
             random= ~ Status | Penguin,
         correlation=corAR1() )

And here is the result I get:
Reached the total allocation of 1022 Mb: see help (memory.size)

Moreover I should add interactions between fixed effects.

Before I feel totally depressed, is there anybody who could help me ?

Thank you in advance

MiKL

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