[R-sig-ME] Binary response ordering

[ONKELINX, Thierry]

Is this homework? The data and the analysis look very similar to the one
is this post
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q3/004203.html

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium

tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
  

> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces at r-project.org 
> [mailto:r-sig-mixed-models-bounces at r-project.org] Namens John Haart
> Verzonden: woensdag 4 augustus 2010 10:54
> Aan: r-sig-mixed-models at r-project.org
> Onderwerp: [R-sig-ME] Binary response ordering
> 
> Dear List,
> 
> I have a quick question regarding the setup of my data for 
> analysis with a glmm.  I hope this is the appropriate list, i 
> apologise if it is not.
> 
> I have a response variable, TRUE or FALSE. I have coded this 
> as 0 = False and 1 = TRUE in excel.
> 
> I have 3 categorical factors with C,D and E
> 
> I then read in the data frame and run the model as follows-
> 
> lmer(trueorfalse~1+(1|A/B) + C + D+ E ,family=binomial)
> 
> And this is the output
> 
> Generalized linear mixed model fit by the Laplace approximation
> Formula: threatornot ~ 1 + (1 | A/B) + C + D+  E ,family=binomial)
>   AIC  BIC logLik deviance
>  1410 1450 -696.8     1394
> Random effects:
>  Groups       Name        Variance   Std.Dev.  
>  family:order (Intercept) 6.7869e-01 8.2382e-01
>  order        (Intercept) 7.8204e-11 8.8433e-06
> Number of obs: 1116, groups: A:B, 43; B, 9
> 
> Fixed effects:
>             Estimate Std. Error z value Pr(>|z|)  
> (Intercept)  0.11281    0.42232   0.267   0.7894  
> C1   -0.02414    0.19964  -0.121   0.9038 
> D2  -0.16482    0.38602  -0.427   0.6694  
> E2       0.95381    0.54316   1.756   0.0791 .
> E3      0.75733    0.87275   0.868   0.3855  
> E4       0.03044    0.47328   0.064   0.9487  
> 
> What i am unsure about is the inference, if a term is 
> significant does this relate to TRUE or FALSE?
> 
> I.E E2 has a p value of 0.079, does this 0.079 relate to the 
> probability of it resulting in a true or false response? Does 
> it matter how i code the input i.e FALSE = 1, TRUE =2 for instance?
> 
> Maybe i am reading the output wrong?
> 
> Thanks
> 
> John
> 
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list 
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> 

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