[R] How can we predict differences in a slope, given that the random component was significant?
Dieter Menne
dieter.menne at menne-biomed.de
Sun Dec 14 11:18:51 CET 2008
Sachi Ito wrote:
>
> Using R lme function, I found that both fixed and random effects of
> variable
> A on variable B are significant.
>
It would be good if you could tell us how you found out that "the random
effects" were significant. I must have missed something here.
Sachi Ito wrote:
>
> Now, I'd like to analyze what variables are predicting differences in the
> slope.
>
In the slightly modified standard example coming with lme, the line
age:SexFemale tells us that "girls grow slower".
Dieter
library(nlme)
fm2 <- lme(distance ~ age * Sex, data = Orthodont, random = ~ 1)
Fixed effects: distance ~ age * Sex
Value Std.Error DF t-value p-value
(Intercept) 16.3 0.98 79 16.7 0.000
age 0.8 0.08 79 10.1 0.000
SexFemale 1.0 1.54 25 0.7 0.508
age:SexFemale -0.3 0.12 79 -2.5 0.014
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