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###
# Varying model formulations (other than formulas). Using and subsetting the
# 'varying' variables.
###
library(nlme)
library(MuMIn)
# from example(corSpher)
fm1BW.lme <- lme(weight ~ Time * Diet, BodyWeight, random = ~ Time)
# generate model selection table:
fm1BW.dd <- dredge(fm1BW.lme,
# fix all terms in all models:
fixed = TRUE,
varying = list(
# vary correlation structure:
correlation = alist(exp = corExp(form = ~ Time),
spher = corSpher(form = ~ Time),
NULL ),
# vary heteroscedasticity structure:
weights = alist(vPower = varPower(),
none = NULL )
),
# additional constraint (regardless of whether it makes sense or not):
# include either heteroscedasticity or correlation structure (but not both).
# Note use of 'is.null' for unnamed item, and "none" when named.
subset = xor(is.null(V(correlation)), V(weights) == "none"),
# global model was fitted with method = "REML" (the default), but for model
# selection we use AICc of a ML model. This additional argument is passed to
# AICc.
REML = FALSE)
print(fm1BW.dd)
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