I want to compare the following two models in AIC
(Treat, Spotter are categorial, p is pressure, Pain is
continuous)
PainW.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat,
weights=varPower(form=~Pain))
# AIC= -448
Pain.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat)
#AIC = -19.7
Note the huge differences in AIC, and the estimated power of 6.
A plot of the residual does not show an unusual patterns for
both models.
I do not trust the varPower result, but don't have any rationale
for it.
1) Can I use the AIC at all to compare two weightings?
2) Can I trust such a high power estimate? There is definitively
some slight dependency of variance on Pain, but it is not extreme.
3) I tried fitted(.) instead of Pain, but it did no converge after
5 Minutes.
---- Detail ---> PainW.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat,
+ weights=varPower(form=~Pain))> summary(PainW.lme)
Linear mixed-effects model fit by REML
Data: saw
AIC BIC logLik
-448 -413 234
Random effects:
Formula: ~p | Pat
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 6.48e-02 (Intr)
p 4.19e-06 0
Residual 1.65e-01
Variance function:
Structure: Power of variance covariate
Formula: ~Pain
Parameter estimates:
power
5.89
Fixed effects: Pain ~ p + Treat * Spotter
Value Std.Error DF t-value p-value
(Intercept) 0.747 0.01753 214 42.6 <.0001
p 0.000 0.00017 214 1.4 0.164
TreatXen -0.003 0.00555 214 -0.5 0.587
SpotterSp+ -0.015 0.02492 29 -0.6 0.551
TreatXen:SpotterSp+ 0.011 0.00847 214 1.4 0.177
Correlation:
(Intr) p TretXn SpttS+
p -0.151
TreatXen -0.163 -0.088
SpotterSp+ -0.685 -0.020 0.125
TreatXen:SpotterSp+ 0.106 0.062 -0.655 -0.194
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.473 -0.180 0.229 0.839 2.443
Number of Observations: 248
Number of Groups: 31
> Pain.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat)
> summary(Pain.lme)
Linear mixed-effects model fit by REML
Data: saw
AIC BIC logLik
-19.7 11.8 18.8
Random effects:
Formula: ~p | Pat
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 0.00748 (Intr)
p 0.00746 0.144
Residual 0.18544
Fixed effects: Pain ~ p + Treat * Spotter
Value Std.Error DF t-value p-value
(Intercept) 0.751 0.0360 214 20.87 <.0001
p 0.008 0.0017 214 4.62 <.0001
TreatXen -0.047 0.0328 214 -1.45 0.1497
SpotterSp+ 0.067 0.0489 29 1.36 0.1839
TreatXen:SpotterSp+ -0.119 0.0472 214 -2.51 0.0127
Correlation:
(Intr) p TretXn SpttS+
p -0.358
TreatXen -0.444 -0.007
SpotterSp+ -0.637 -0.013 0.329
TreatXen:SpotterSp+ 0.305 0.017 -0.696 -0.490
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-3.001 -0.586 -0.112 0.380 3.591
Number of Observations: 248
Number of Groups: 31 >
>
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