R-users -
I must preface this question by saying that I'm a relative newbie to
both R and mixed-modeling.
I'm using lme fit an ANCOVA-like model. My data consist of bone
length measurements for a developmental series of two capuchin monkey
species. I'm interested in whether the rate of bone length scaling
to body mass (i.e., growth) differs between species.
My call to lme looks like this:
model=lme(lghumlen ~ species + lgbmkg, random= ~ lgbmkg | id,
correlation=corCAR1(), weights=varExp())
where lghumlen is the log of humerus length and lgbmkg is the log of
body mass.
If I ask for a summary of my model, I get this from the table of
fixed effects:
Fixed effects: lghumlen ~ species + lgbmkg
Value Std.Error DF t-value p-value
(Intercept) 3.567226 0.02059541 540 173.20495 0.000
speciesCebus apella -0.096348 0.04523323 16 -2.13004 0.049
lgbmkg 0.532745 0.01804411 540 29.52459 0.000
So the difference between species looks to be barely significant. I
get the same result using the fit.contrast function from the gtools
package:
fit.contrast(model,"species",coeff=c(1,-1))
Estimate Std. Error t-value Pr(>|t|)
species c=( 1 -1 ) 0.09634857 0.04523308 2.130046 0.04903713
However, if I use the anova function to summary the model, I get a
totally different result:
anova(model)
numDF denDF F-value p-value
(Intercept) 1 540 49336.06 <.0001
species 1 16 0.45 0.5115
lgbmkg 1 540 871.70 <.0001
So the difference between species is not significant at all.
What gives?
Thanks,
Jesse
______________________________________________
Jesse W. Young, Ph.D.
Postdoctoral Research Fellow
Department of Anthropology
University of Texas at Austin
1 University Station C3200
Austin, TX 78712
Phone: (512) 232-6380
Email: [EMAIL PROTECTED]
Web page: https://webspace.utexas.edu/youngjw3/www/
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