R help - Dec 2008 - How can we predict differences in a slope, given that the random component was significant?

Dear R users,

Using R lme function, I found that both fixed and random effects of variable
A on variable B are significant.  Now, I'd like to analyze what variables
are predicting differences in the slope.  In other words, I'd like to know
what variables (e.g., variable C) are predicting individual differences in
the effects of A on B.  I have many data points for A and B for each
individual, whereas I have only one data point for C.
I'd appreciate if anyone could answer the question.

Thank you for your attention.

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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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