we have a 2*3 factorial design, where 2 out of the 3 treatment levels
are tested on each subject on two different days, each before and after a
meal (When)
There is strong evidence for heteroscedascticity. The lme-analysis with
varPower-weighting has significantly lower AIC, estimated power is 1.33.
Estimated values are physiologically reasonable and close to averages for
the unweighted case (e.g. 130.3+184.8=315 for WhenPost at the base level),
but for the weighted case they are not (110.2+134.9=245).
Does using weighted estimates make sense at all? Or did I misuse weighted
lme here? Is there any warning signal I overlooked?
Dieter Menne
-------------------
No weigthing
vTonus.lme<-lme(vTonus~Treat*When,data=to,random=~1|Subj/Day)
AIC BIC logLik
844 864 -413
....
Value Std.Error DF t-value p-value
(Intercept) 130.3 35.3 33 3.69 0.0008
TreatCel 62.3 45.4 16 1.37 0.1890
TreatDic 63.4 45.4 16 1.40 0.1818
WhenPost 184.8 34.8 33 5.32 <.0001
TreatCel:WhenPost -59.8 49.1 33 -1.22 0.2320
TreatDic:WhenPost -68.4 49.1 33 -1.39 0.1735
Correlation:
(Intr) TretCl TretDc WhnPst TrC:WP
TreatCel -0.642
TreatDic -0.642 0.500
WhenPost -0.492 0.383 0.383
TreatCel:WhenPost 0.348 -0.541 -0.271 -0.707
TreatDic:WhenPost 0.348 -0.271 -0.541 -0.707 0.500
With vaPower weighting:
vTonusW.lme<-lme(vTonus~Treat*When,data=to,
+ weights=varPower(form=~vTonus),random=~1|Subj/Day)
AIC BIC logLik
830 852 -405
..
Parameter estimates:
power 1.33
..
Value Std.Error DF t-value p-value
(Intercept) 110.2 20.9 33 5.27 <.0001
TreatCel 54.0 28.5 16 1.90 0.0762
TreatDic 31.9 28.2 16 1.13 0.2744
WhenPost 134.9 26.2 33 5.15 <.0001
TreatCel:WhenPost -84.4 28.7 33 -2.94 0.0060
TreatDic:WhenPost -96.5 32.9 33 -2.93 0.0061
Correlation:
(Intr) TretCl TretDc WhnPst TrC:WP
TreatCel -0.562
TreatDic -0.554 0.386
WhenPost -0.125 0.093 0.091
TreatCel:WhenPost 0.111 -0.119 -0.078 -0.912
TreatDic:WhenPost 0.096 -0.070 -0.195 -0.794 0.724