[R-sig-ME] pvals.fnc + lmer, discrepancy between pvalues

[Nadine Klauke]

Dear R list members,

I´m trying to fit a mixed model with log transformed count
data in R (version 13.0). Here is the model specification:

m1<-lmer(Surv~H*E+HER+G+(1|ID),data=rep1)

The variables are:
Surv ##log-transformed count data
E ## 2-level categorical 
H ##count data
HER ##continous between 0 and 1
G ##count data
random effect: individual ID (individuals repeatedly
measured in differnet years)

The response variable is log-transformed because of
underdispersion with poisson-distribution.
When I inspected the p-values of the model given by
pvals.fnc() I realized that the pMCMC values are completely
different from the pvalues based on a t-distribution (R
output see below). I am aware that
pMCMC values are more reliable for mixed models.
Nevertheless, as far as I get it form the help lists etc,
the values usually do not differ so much. Or am I wrong?
When I calculate pvalues through logliklihood estimations
with anova() pvalues look more similar to those of the
t-distribution. Furthermore, the density plots of the fixed
effects given by pvals.fnc
look normally distributed. Might the different pvalues be
due to small sample size? Should I rather rely on the
logliklihood estimation than on pMCMC? Any advices would be
appreciated.

Thanks a lot.

Nadine


Results given by R:

m1<-lmer(Surv~H*E+HER+G+(1|ID),data=rep1)
summary(m1)
pvals.fnc(m1)

Linear mixed model fit by maximum likelihood 
Formula: Surv ~ H * E + HER + G + (1 | ID) 
Data: rep1 
AIC   BIC logLik deviance REMLdev
9.428 19.49  3.286   -6.572   17.59
Random effects:
  Groups   Name        Variance Std.Dev.
ID       (Intercept) 0.090871 0.301447
Residual             0.003334 0.057741
Number of obs: 26, groups: ID, 19

Fixed effects:
  Estimate Std. Error t value
(Intercept)   -0.87556    0.47112  -1.858
H              0.04135    0.01389   2.976
Eun           -0.52552    0.11116  -4.728
HER            3.45757    0.90229   3.832
G             -0.04023    0.03136  -1.283
H:Eun          0.22855    0.03812   5.996

Correlation of Fixed Effects:
             (Intr) H      Enrfhr HER    Gelege
H            0.467                            
Eun          0.364  0.337                     
HER         -0.950 -0.485 -0.573              
G            0.562  0.222  0.777 -0.767       
H:Eun       -0.550 -0.361 -0.864  0.712 -0.815

pvals.fnc(m1)
$fixed
               Estimate MCMCmean HPD95lower HPD95upper
 pMCMC Pr(>|t|)
(Intercept)    -0.8756  -0.6317    -2.1442     0.9813
0.4034   0.0779
H               0.0414   0.0366    -0.0518     0.1262
0.3992   0.0075
Eunerfahren    -0.5255  -0.1302    -0.5614     0.3020
0.5394   0.0001
HER             3.4576   1.6306    -0.7388     4.0931
0.1766   0.0010
Gelege         -0.0402   0.1261     0.0005     0.2523
0.0516   0.2143
H:Eunerfahren   0.2286   0.0506    -0.1330     0.2172
0.5524   0.0000

$random
Groups        Name Std.Dev. MCMCmedian MCMCmean HPD95lower
HPD95upper
1       ID (Intercept)   0.3014     0.0320   0.0464
    0.0000     0.1422
2 Residual               0.0577     0.3015   0.3076
    0.2124     0.4149

m1<-lmer(Surv~H*E+HER+G+(1|ID),REML=F,data=rep1)
m2<-lmer(Surv~H+E+HER+G+(1|ID),REML=F,data=rep1)
anova(m2,m1)

#Models:
#m2: Surv ~ H + E + HER + G + (1 | ID)
#m1: Surv ~ H * E + HER + G + (1 | ID)
#Df     AIC    BIC  logLik  Chisq Chi Df Pr(>Chisq)   
#m2  7 17.8965 26.703 -1.9482                            
#m1  8  9.4276 19.492  3.2862 10.469      1   0.001214 **
 # ---
 # Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1
‘ ’ 1 


m1<-lmer(Surv~H*E+HER+G+(1|ID),REML=F,data=rep1)
m3<-lmer(Surv~H*E+G+(1|ID),REML=F,data=rep1)
anova(m3,m1)
#Models:
#m3: Surv ~ H * E + Gelege + (1 | ID)
#m1: Surv ~ H * E + HER + Gelege + (1 | ID)
#Df     AIC    BIC  logLik  Chisq Chi Df Pr(>Chisq)   
#m3  7 16.2181 25.025 -1.1090                            
#m1  8  9.4276 19.492  3.2862 8.7905      1   0.003028 **
  ---
#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1
‘ ’ 1 

m1<-lmer(Surv~H*E+HER+G+(1|ID),REML=F,data=rep1)
m4<-lmer(Surv~H*E+HER+(1|ID),REML=F,data=rep1)
anova(m4,m1)
#m4: Surv ~ H * E + HER + (1 | ID)
#m1: Surv ~ H * E + HER + G + (1 | ID)
#Df    AIC    BIC logLik  Chisq Chi Df Pr(>Chisq)
#m4  7 8.3834 17.190 2.8083                         
#m1  8 9.4276 19.492 3.2862 0.9559      1     0.3282