random effects

Dear Tom,
my apologies for the long delay between you request and this answer.
First of all, why not include precipitation ("Precip") as a main effect too?

Using libray "nlme" and lme():
Random intercepts per station:
random= ~1|station
Don't forget the vertical bar!
Including random slope:
random= ~Precip|station
or
random= ~1+Precip|station

Make sure that all stations have different names, otherwise you run the risk that stations with identical names at different distances will be considered as a single station.

You could also consider to use the updated library now called "lme4".
Then the random effects are simply included as extra terms in the model statement.

Cheers, Tom

random components of lme-model

Hi everyone,

I'm currently analysing a large dataset with measurements of nutrients in the Dutch marine systems in R. I have a variable "OffshoreDist" representing the distance from the sampling location to the coast. I also calculated the total amount of precipitation over The Netherlands and called this variable "Precip".What I want to do is link mean annual dissolved inorganic nitrogen (DIN) to the amount of precipitation. I expect that this relationship is dependent on the offshore distance. Distance classes can contain 1 or more sampling stations.

That's why I propose these two models with different random components:
model1 <- lme(log(DIN) ~ Precip:factor(OffshoreDist) + factor(OffshoreDist),
random= ~1station)
model2 <- lme(log(DIN) ~ Precip:factor(OffshoreDist) + factor(OffshoreDist),
random= ~Precipstation)

Are these the right models for random intercepts per station (model1) and random slope+intercept per station (model2)? Or should this be something like:

random = ~ factor(OffshoreDist) -1 station (model 1)
and
random = ~ Precip:factor(OffshoreDist)station (model 2)

I've read Pinheiro & Bates, 2000 which is a good reference, but explanation of the formulae only by means of examples does not seem to work for me.

Thanks in advance.

Cheers,

Tom Van Engeland

moved to new blogger

Hi,
I've moved the blog into the new google template. Everyone logging in will be invited to move her/his account too. A nice aspect of the change is that the blog can now be searched easily!
Cheers, Tom

F lmer

Hi Marie,
do you really need to use the quasibinomial distribution? Is there evidence of overdispersion in the maximal model you fit?

If not, then you can use the binomial distribution.
That allows you to use likelihood-ratio tests for the fixed and random effects.
using
anova(model,modelsimplified,test="Chisq")

If there is clear overdispersion, then I guess the best thing to do would be resampling according to the model, if that works for the quasibinomial "distribution". You need to use mcmcsamp() for that.
Otherwise, it will become hand-work to get some of those hotly debated F-ratios out....
Cheers, Tom

Longitudinal Data

Finally, the participants in the course Longitudinal and Incomplete Data have been invited. Transcripts for R of the SAS code presented during the course will be added to the weblog soon.
Cheers, Tom