> 1. The first I used to do in SPSS and I would like to be able
> to do it in R as well.
> This is the hierarchical model I would like to use: a continuous
> variable explained by factor A(fixed) + factor B(random)
> nested in A + factor C (random) nested in factor B (which is nested
> in A).
You would need the following, I guess:
lme (var ~ A, data= NameOfDataFrame, random= ~ 1 | B/C)
This means that C is nested in B. If all C within B have the same levels of A,
the model automatically treats B as nested in A(but you can check that based on
the degrees of freedom in the output).
> 2. the same model but than for count data (like 15 out of 30,
> 23 out of 60) instead of the continous variable(I know the basics
> of glm in R)
I guess, this can be viewed as a data set with binary outcomes and thus modeled
by a generalised mixed-effects model with a binomial distribution using glmmPQL
(in library MASS) or lmer (in library lme4). Please refer to:
@Book{Pin:00a,
author = {Pinheiro, Jose C and Bates, Douglas M},
title = {Mixed-Effects Models in {S} and {S}-{P}{L}{U}{S}},
publisher = {Springer},
year = {2000},
address = {New York}
}
@BOOK {Ven:02,
AUTHOR = {Venables, W N and Ripley, B D},
TITLE = {Modern Applied Statistics with {S}},
PUBLISHER = {Springer},
YEAR = {2002},
ADDRESS = {New York},
EDITION = {fourth}
}
@Article{Rnews:Bates:2005,
author = {Douglas Bates},
title = {Fitting Linear Mixed Models in {R}},
journal = {R News},
year = 2005,
volume = 5,
number = 1,
pages = {27--30},
month = {May},
url = {http://CRAN.R-project.org/doc/Rnews/},
}
and: http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests
Do not be surprised if the results are not identical to those in SPSS ...
Regards, Lorenz Gygax
-
Swiss Federal Veterinary Office
Centre for proper housing of ruminants and pigs
Agroscope Reckenholz-Tänikon Research Station ART
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