[R] post hoc comparisons on NLME for longitudinal data

Humberto Dutra hpdutra at yahoo.com
Thu Jul 3 09:38:39 CEST 2008


I am trying to fit a non linear mixed effect model but I also want to do a post hoc comparison.
My
data is binary and consist of recording mice track prints on plates
plates in plots that submited to one of 4 different treatments (fruits
and vegetation complexity manipulated for two levels each. The design
is random blocks repeated measures with presence or absence of track
prints as a response variable. Treatment is fixeed, time is fixed, and
block is a random effect. I would like to know how I could do a post
hoc comparison to see which months had a significant effect of an
specific treatment. I searche on Bates and Pinheiro book but they don't
have an example of it.


> names (kirk)
[1] "Obs" 
"track" "time"  "block" "treat" ##Obs is the track plate identity,
track is response variable, time is the month in which it was recorded
and treat is one of the four treatments.##
> mice <- lmer(track ~treat*time+(1|block), family=binomial, method = "PQL",data = kirk)
> mice
Generalized linear mixed model fit using PQL 
Formula: track ~ treat * time + (1 | block) 
   Data: kirk 
Family: binomial(logit link)
   AIC  BIC logLik deviance
956.9 1002 -469.4    938.9
Random effects:
Groups Name        Variance Std.Dev.
block  (Intercept) 0.58182  0.76277 
number of obs: 1168, groups: block, 3

Estimated scale (compare to  1 )  1.000414 

Fixed effects:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -1.39258    0.54124  -2.573 0.010084 *  
treatFIVR      -1.69691    0.53920  -3.147 0.001649 ** 
treatFRVI      -0.86542    0.49603  -1.745 0.081039 .  
treatFRVR      -1.54753    0.59766  -2.589 0.009617 ** 
time                -0.05365    0.08459  -0.634 0.525957    
treatFIVR:time  0.43627    0.13169   3.313 0.000924 ***
treatFRVI:time  0.23028    0.12637   1.822 0.068418 .  
treatFRVR:time  0.16126    0.15067   1.070 0.284502    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Correlation of Fixed Effects:
                   (Intr)    trFIVR trFRVI trFRVR   time     tFIVR: tFRVI:
treatFIVR    -0.327                                          
treatFRVI    -0.359   0.359                                  
treatFRVR   -0.297   0.298    0.324                            
time             -0.506   0.503    0.548     0.454                    
tretFIVR:tm   0.319  -0.906  -0.351    -0.292   -0.640              
tretFRVI:tm   0.336  -0.337  -0.896    -0.304   -0.668  0.429      
tretFRVR:tm   0.283 -0.282  -0.307    -0.899   -0.561  0.359  0.375

thank you for your help
Humberto Dutra


==========================================================
'Discipline - Success doesn't just happen. You have to be intentional about it, and that takes discipline.' - John Maxwell




 Humberto Dutra


==========================================================
'Discipline - Success doesn't just happen. You have to be intentional about it, and that takes discipline.' - John Maxwell







More information about the R-help mailing list