Dear all,
Using the data set and code below, I am interested in modelling how egg
temperature (egg.temp)
is related to energy expenditure (kjday) and clutch size (treat) in
incubating birds using the
lme-function. I wish to generate the F-distribution for my model, and have
tried to do so using
the anova()-function. However, in the resulting anova-table, the parameter
kjday has gone from
being highly non-signiicant in the lme-expression, to all of a sudden being
significant at the
0.05 level. At the same time, "treat" retains its original p-value. I've
tried to understand why,
but can't really figure it out. So, what has happened and why? How to best
interpret it?
Further, any advice on how to best generate F-distributions from the
lme-function is most appreciated.
Many thanks in advance,
Andreas Nord
Sweden
ind treat egg.temp kjday
79 2 27.33241 42.048
42 2 30.73269 41.760
10 2 29.54986 38.304
206 2 31.78947 45.216
23 2 29.69114 40.896
24 2 36.48199 46.944
45 2 29.76454 44.064
29 2 30.56510 42.912
78 2 27.71468 43.200
79 3 25.88227 45.216
42 3 30.95983 44.640
10 3 28.13296 45.216
206 3 31.77147 45.216
23 3 27.50000 42.336
5 3 28.16205 51.264
24 3 34.69391 48.960
45 3 28.79778 46.368
368 3 26.18006 45.792
29 3 29.75208 45.216
78 3 25.28393 43.200
44 3 23.32825 44.640
# lme-model with "individual" as random factor
> incub.lme2<-lme(egg.temp~kjday+treat,random=~1|ind,data=incub.df)
Fixed effects: egg.temp ~ kjday + treat
Value Std.Error DF t-value p-value
(Intercept) 24.937897 6.662475 11 3.743038 0.0032
kjday 0.108143 0.152540 7 0.708945 0.5013
treat3 -1.506605 0.485336 7 -3.104254 0.0172
#generating an anova table to get the F-distribution
> anova(incub.lme2)
numDF denDF F-value p-value
(Intercept) 1 11 1176.6686 <.0001
kjday 1 7 5.7060 0.0483
treat 1 7 9.6364 0.0172
>
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