Hi there,
I try to compare the mixed model package "lme" by Splus and R. I used the
dataset "Ovary" and the following code assuming AR(1) model for the error term:
lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), data=Ovary, random =
pdDiag(~sin(2*pi*Time) ) , correlation=corAR1() )
But I got different results! And then I used a simpler model:
lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), data=Ovary, random =
pdDiag(~sin(2*pi*Time)) )
This time I got the same output. I wonder the reason for the inconsistence
between R and Splus. I attached the results as follows. Can someone tell me
how to do it correctly in R? Thanks!
Lei
Result from R:
******************************************************************
Linear mixed-effects model fit by REML
Data: Ovary
Log-restricted-likelihood: -775.2224
Fixed: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time)
(Intercept) sin(2 * pi * Time) cos(2 * pi * Time)
12.1895808 -2.9473432 -0.8807113
Random effects:
Formula: ~sin(2 * pi * Time) | Mare
Structure: Diagonal
(Intercept) sin(2 * pi * Time) Residual
StdDev: 2.807293 0.03630784 3.665217
Correlation Structure: AR(1)
Formula: ~1 | Mare
Parameter estimate(s):
Phi
0.6073908
Number of Observations: 308
Number of Groups: 11
*****************************************************************************************
Result from Splus:
*****************************************************************************************
Linear mixed-effects model fit by REML
Data: Ovary
Log-restricted-likelihood: -774.724
Fixed: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time)
(Intercept) sin(2 * pi * Time) cos(2 * pi * Time)
12.18809 -2.985282 -0.8777629
Random effects:
Formula: ~ sin(2 * pi * Time) | Mare
Structure: Diagonal
(Intercept) sin(2 * pi * Time) Residual
StdDev: 2.858478 1.25802 3.507097
Correlation Structure: AR(1)
Formula: ~ 1 | Mare
Parameter estimate(s):
Phi
0.5722019
Number of Observations: 308
Number of Groups: 11
******************************************************************************************
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