Dear members of the R-sig-mixed-models-group,

 

Martin Maechler encouraged me to ask you for help with the following
problem:

 

I'm estimating GLMMs with the Laplace approximation (lmer) of the lme4
package with the following scientific question:

"Which ecological factors (roost, reproductive state, age etc) are
influencing the ectoparasite infestation (e.g. number of ticks) of a bat
individual, when season is taken as a random effect?"

My response variable "Ticks" is a count data showing overdispersion, so
I use familiy=quassipoisson.

My full model is as follows:

 

mq1 <- lmer (TICKS ~ Roost+ Repro+ Age+(1|Season), family=quasipoisson,
data=mydata)

 

For model validation I use QAIC and ddQAIC as suggested in Bolker et al
2009, doi:10.1016/j.tree.2008.10.008. And it works easily to select
meaningful fixed effect variables.

My problem is that using the summary command no p-value of the
calculated t-test is shown. Without a p-value I am not able to decide
which levels of a categorical variable (like roost with 7 levels: A, B,
C...; or repro with 5 levels) has an effect on the ectoparasite
infestation and which level has not.

My data frame looks as follows: 

 

> str(mydata)

'data.frame':   159 obs. of  6 variables:

 $ Number: int  1 2 4 5 6 7 8 12 13 14 ...

 $ Season: Factor w/ 6 levels "2006_9","2007_11",..: 5 5 5 5 5 6 5 5 5 5
...

 $ Roost : Factor w/ 7 levels "A","B","BCI",..: 4 3 4 4 3 4 4 4 4 3 ...

 $ Repro : Factor w/ 5 levels "f_lact","f_nr",..: 4 3 2 2 5 3 4 4 2 5
...

 $ Age   : int  0 0 1 0 0 0 0 0 0 0 ...

 $ Ticks : num  1 5 2 4 0 6 1 6 4 0 ...

 

 

And my summery looks as follows: 

 

>  summary (mq1)

Generalized linear mixed model fit by the Laplace approximation 

Formula: Ticks ~ Roost + Repro + Age + (1 | Season) 

   Data: mydata 

 AIC BIC logLik deviance

 449 492   -210      421

Random effects:

 Groups   Name        Variance Std.Dev.

 SEASON   (Intercept) 2.02     1.42    

 Residual             6.12     2.47    

Number of obs: 159, groups: SEASON, 6

 

Fixed effects:

                   Estimate Std. Error t value

(Intercept)          0.9871     0.7192    1.37

RoostA              -2.1886     0.9715   -2.25

RoostB               0.0199     0.4478    0.04

RoostC              -0.2225     0.4155   -0.54

RoostD               0.3762     0.5153    0.73

RoostE              -0.9135     0.6630   -1.38

RoostF               3.0358     0.7480    4.06

REPROm_nr            1.0524     0.3780    2.78

REPROf_nr            0.9062     0.3772    2.40

REPROf_lact          0.4524     0.4207    1.08

REPROf_preg          0.4302     0.4916    0.87

Age                  0.1861     0.1829    1.02

 

Is there any R-Code I can use to estimate the significance of the
t-value?

 

Thanks for your help!

Sincerely Julia Schad

 

 

 

Julia Schad, PhD Student

Evolutionary Genetics, Leibniz Institute for Zoo and Wildlife Research, 

Alfred-Kowalke Str. 17 10315 Berlin, Germany

Phone: +49 (0) 30 5168 712

 


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