Dear all,
We are currently evaluating data from observations of bulls (4 groups with 5
animals each) which have been kept on varying areas where they lie down
(three different ground surfaces and four different slopes). All animals
were kept on all possible combinations of surfaces and slopes and we attempt
to model some changes over time as well.
Thus we started out in the following way (SPlus 6.1 for Windows):
res.lme <- lme (fixed= response ~ slope * surface + time + time^2,
random= ~ . | group/bull)
Slope and time are included as numeric variables, surface as a factor.
To get a better interpretation of the estimated effects, we want to use:
options (contrasts= c (factor= 'contr.treatment', ordered= 'contr.poly'))
I was surprised to see that anova (res.lme, type= 'marginal') gives very
different p-values (in one example 0.85 instead of 0.04) for the numeric
variable 'slope' and also summary (res.lme) gives quite different estimates
for 'slope'. The results of anova (res.lme) are identical with the default
and chosen contrasts.
Any idea what could happen here? I always thought that changing contrasts
should not affect the overall significance of a variable, least of all the
one of a numeric variable.
I tried to replicate this observation with a small data set which I could
have included in this e-mail but did not succeed quickly to replicate the
problem. If necessary I can provide the data of an exemplary response
variable.
Many thanks, Lorenz
-
Lorenz Gygax, Tel: +41 52 368 33 84 / lorenz.gygax@fat.admin.ch
Center for proper housing of ruminants and pigs
Swiss Veterinary Office, FAT, CH-8356 Tänikon / Switzerland
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