R: diagramme des Interactions avec une constante et une variable catégorique pour un GLMM (lme4)

Je voudrais faire un diagramme des interactions pour représenter visuellement la différence ou la similitude dans les pentes de l'interaction d'une variable catégorielle (4 niveaux) et une assiette variable en continu à partir des résultats d'un modèle de régression.

with(GLMModel, interaction.plot(continuous.var, categorical.var, response.var))
N'est pas ce que je recherche. Elle produit d'un complot dans lequel les changements de pente pour chaque valeur de la variable continue. Je suis à la recherche de faire une intrigue avec des constantes et des pistes dans la suite de l'intrigue:

R: diagramme des Interactions avec une constante et une variable catégorique pour un GLMM (lme4)

Des idées?

Je adapter à un modèle de la forme fit<-glmer(resp.var ~ cont.var*cat.var + (1|rand.eff) , data = sample.data , poisson)
Voici quelques exemples de données:

structure(list(cat.var = structure(c(4L, 4L, 1L, 4L, 1L, 2L, 
1L, 1L, 1L, 1L, 4L, 1L, 1L, 3L, 2L, 4L, 1L, 1L, 1L, 2L, 1L, 2L, 
2L, 1L, 3L, 1L, 1L, 2L, 4L, 1L, 2L, 1L, 1L, 4L, 1L, 3L, 1L, 3L, 
3L, 4L, 3L, 4L, 1L, 3L, 3L, 1L, 2L, 3L, 4L, 3L, 4L, 2L, 1L, 1L, 
4L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 4L, 4L, 3L, 3L, 1L, 3L, 3L, 
3L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 4L, 1L, 3L, 4L, 1L, 1L, 4L, 
1L, 3L, 1L, 1L, 3L, 2L, 4L, 1L, 4L, 1L, 4L, 4L, 4L, 4L, 2L, 4L, 
4L, 1L, 2L, 1L, 4L, 3L, 1L, 1L, 3L, 2L, 4L, 4L, 1L, 4L, 1L, 3L, 
2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 
2L, 2L, 1L, 1L, 2L, 3L, 1L, 4L, 4L, 4L, 1L, 4L, 4L, 3L, 2L, 4L, 
1L, 3L, 1L, 1L, 4L, 4L, 2L, 4L, 1L, 1L, 3L, 4L, 2L, 1L, 3L, 3L, 
4L, 3L, 2L, 3L, 1L, 4L, 2L, 2L, 1L, 4L, 1L, 2L, 3L, 4L, 1L, 4L, 
2L, 1L, 3L, 3L, 3L, 4L, 1L, 1L, 1L, 3L, 1L, 3L, 4L, 2L, 1L, 4L, 
1L, 1L, 1L, 2L, 1L, 1L, 4L, 1L, 3L, 1L, 2L, 1L, 4L, 1L, 2L, 4L, 
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 4L, 1L, 4L, 3L, 
3L, 3L, 4L, 1L, 3L, 1L, 1L, 4L, 4L, 4L, 4L, 2L, 1L, 1L, 3L, 2L, 
1L, 4L, 4L, 2L, 4L, 2L, 4L, 1L, 3L, 4L, 1L, 1L, 2L, 3L, 2L, 4L, 
1L, 1L, 3L, 4L, 2L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 4L, 1L, 4L, 
2L, 4L, 3L, 4L, 2L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 1L, 4L, 4L, 1L, 
4L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 2L, 2L, 1L, 1L, 4L, 
1L, 4L, 3L, 1L, 2L, 1L, 4L, 2L, 4L, 4L, 1L, 2L, 1L, 1L, 1L, 4L, 
1L, 4L, 1L, 2L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 4L, 3L, 1L, 4L, 1L, 
2L, 4L, 1L, 1L, 3L, 3L, 2L, 4L, 4L, 1L, 1L, 2L, 2L, 1L, 2L, 4L, 
3L, 4L, 4L, 4L, 4L, 1L, 3L, 1L, 2L, 2L, 2L, 4L, 2L, 3L, 4L, 1L, 
3L, 2L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 1L, 
1L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 3L, 3L, 2L, 1L, 3L, 2L, 1L, 
1L, 1L, 4L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 
1L, 3L, 1L, 3L, 1L, 4L, 3L, 1L, 4L, 4L, 2L, 4L, 1L, 1L, 2L, 4L, 
4L, 2L, 3L, 4L, 4L, 3L, 1L, 4L, 1L, 2L, 4L, 1L, 1L, 4L, 1L, 1L, 
1L, 1L, 1L, 3L, 4L, 1L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 4L, 4L, 1L, 
1L, 4L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 4L, 2L, 
3L, 1L, 1L, 1L, 4L, 1L, 1L, 4L, 4L, 4L, 1L, 1L, 1L, 1L), .Label = c("A", 
"B", "C", "D"), class = "factor"), cont.var = c(-0.0682900527296927, 
0.546320421837542, -0.273160210918771, -0.887770685486005, 0.136580105459385, 
0.75119058002662, 0.546320421837542, -0.273160210918771, -0.682900527296927, 
0.136580105459385, 0.75119058002662, 0.75119058002662, 0.75119058002662, 
0.341450263648464, 0.75119058002662, 0.546320421837542, 0.546320421837542, 
-0.478030369107849, -0.478030369107849, -0.682900527296927, -0.682900527296927, 
0.546320421837542, -0.478030369107849, -0.0682900527296927, 0.136580105459385, 
0.136580105459385, 0.75119058002662, -0.478030369107849, 0.75119058002662, 
-0.887770685486005, 0.136580105459385, -0.478030369107849, 0.341450263648464, 
-0.682900527296927, -0.478030369107849, 0.341450263648464, -0.478030369107849, 
0.546320421837542, 0.75119058002662, -0.478030369107849, -0.273160210918771, 
0.546320421837542, -0.682900527296927, 0.75119058002662, -0.478030369107849, 
-0.887770685486005, 0.136580105459385, -0.887770685486005, -0.0682900527296927, 
-0.478030369107849, 0.546320421837542, 0.75119058002662, 0.136580105459385, 
-0.273160210918771, -0.273160210918771, 0.75119058002662, -0.682900527296927, 
0.136580105459385, -0.273160210918771, -0.273160210918771, 0.136580105459385, 
0.136580105459385, 0.341450263648464, 0.136580105459385, -0.273160210918771, 
-0.273160210918771, -0.682900527296927, -0.887770685486005, -0.0682900527296927, 
0.136580105459385, -0.0682900527296927, -0.273160210918771, -0.273160210918771, 
0.341450263648464, 0.75119058002662, -0.682900527296927, -0.0682900527296927, 
-0.273160210918771, -0.887770685486005, -0.0682900527296927, 
0.75119058002662, 0.546320421837542, 0.75119058002662, 0.75119058002662, 
-0.887770685486005, 0.341450263648464, 0.75119058002662, -0.887770685486005, 
0.136580105459385, -0.273160210918771, 0.546320421837542, 0.546320421837542, 
-0.682900527296927, 0.75119058002662, 0.136580105459385, -0.0682900527296927, 
-0.478030369107849, 0.75119058002662, -0.478030369107849, 0.341450263648464, 
0.136580105459385, -0.0682900527296927, -0.478030369107849, -0.0682900527296927, 
-0.0682900527296927, 0.546320421837542, -0.273160210918771, 0.75119058002662, 
0.341450263648464, 0.546320421837542, -0.478030369107849, 0.136580105459385, 
-0.887770685486005, -0.273160210918771, -0.273160210918771, -0.478030369107849, 
-0.478030369107849, 0.75119058002662, -0.682900527296927, -0.0682900527296927, 
0.546320421837542, 0.75119058002662, 0.546320421837542, 0.136580105459385, 
-0.478030369107849, 0.136580105459385, 0.546320421837542, -0.478030369107849, 
-0.0682900527296927, -0.0682900527296927, 0.546320421837542, 
-0.273160210918771, 0.136580105459385, -0.0682900527296927, 0.75119058002662, 
-0.0682900527296927, 0.546320421837542, -0.887770685486005, -0.0682900527296927, 
-0.682900527296927, -0.478030369107849, -0.478030369107849, -0.682900527296927, 
0.75119058002662, 0.341450263648464, -0.0682900527296927, 0.341450263648464, 
-0.0682900527296927, -0.887770685486005, -0.887770685486005, 
-0.273160210918771, -0.0682900527296927, 0.546320421837542, -0.0682900527296927, 
-0.0682900527296927, 0.75119058002662, -0.0682900527296927, -0.273160210918771, 
-0.478030369107849, 0.546320421837542, 0.546320421837542, 0.546320421837542, 
0.341450263648464, 0.136580105459385, -0.478030369107849, 0.136580105459385, 
0.136580105459385, 0.136580105459385, -0.478030369107849, -0.273160210918771, 
-0.273160210918771, -0.273160210918771, 0.341450263648464, -0.273160210918771, 
-0.0682900527296927, 0.136580105459385, 0.546320421837542, -0.478030369107849, 
-0.273160210918771, 0.546320421837542, 0.546320421837542, -0.273160210918771, 
-0.0682900527296927, 0.341450263648464, 0.546320421837542, -0.0682900527296927, 
0.136580105459385, -0.478030369107849, 0.75119058002662, -0.478030369107849, 
-0.682900527296927, -0.478030369107849, 0.136580105459385, -0.273160210918771, 
-0.0682900527296927, -0.887770685486005, -0.887770685486005, 
0.546320421837542, -0.273160210918771, 0.546320421837542, -0.478030369107849, 
0.546320421837542, -0.0682900527296927, 0.75119058002662, -0.273160210918771, 
0.546320421837542, 0.341450263648464, -0.0682900527296927, -0.0682900527296927, 
-0.0682900527296927, -0.887770685486005, 0.136580105459385, -0.273160210918771, 
-0.478030369107849, 0.75119058002662, 0.341450263648464, 0.546320421837542, 
-0.273160210918771, 0.546320421837542, 0.75119058002662, -0.273160210918771, 
0.75119058002662, 0.546320421837542, -0.273160210918771, -0.273160210918771, 
0.75119058002662, -0.273160210918771, -0.0682900527296927, 0.136580105459385, 
-0.478030369107849, 0.75119058002662, 0.75119058002662, -0.887770685486005, 
-0.887770685486005, 0.546320421837542, -0.682900527296927, -0.887770685486005, 
0.136580105459385, 0.75119058002662, 0.75119058002662, -0.478030369107849, 
0.136580105459385, 0.75119058002662, -0.273160210918771, -0.682900527296927, 
-0.273160210918771, 0.136580105459385, 0.546320421837542, -0.682900527296927, 
-0.478030369107849, 0.136580105459385, -0.682900527296927, -0.0682900527296927, 
-0.478030369107849, 0.136580105459385, -0.887770685486005, -0.273160210918771, 
-0.0682900527296927, -0.273160210918771, -0.887770685486005, 
0.546320421837542, 0.546320421837542, -0.478030369107849, -0.273160210918771, 
-0.0682900527296927, 0.136580105459385, -0.478030369107849, 0.75119058002662, 
0.341450263648464, 0.136580105459385, 0.136580105459385, 0.75119058002662, 
0.136580105459385, -0.0682900527296927, 0.546320421837542, -0.0682900527296927, 
-0.887770685486005, 0.75119058002662, 0.75119058002662, 0.546320421837542, 
-0.887770685486005, -0.0682900527296927, -0.682900527296927, 
-0.682900527296927, 0.75119058002662, 0.75119058002662, -0.478030369107849, 
0.546320421837542, -0.273160210918771, 0.75119058002662, -0.0682900527296927, 
0.546320421837542, -0.0682900527296927, -0.273160210918771, 0.546320421837542, 
0.75119058002662, -0.0682900527296927, 0.546320421837542, -0.682900527296927, 
-0.273160210918771, -0.0682900527296927, -0.478030369107849, 
-0.478030369107849, 0.136580105459385, -0.273160210918771, 0.136580105459385, 
0.546320421837542, 0.75119058002662, -0.273160210918771, 0.341450263648464, 
-0.273160210918771, 0.136580105459385, 0.546320421837542, 0.546320421837542, 
0.136580105459385, 0.136580105459385, -0.682900527296927, 0.341450263648464, 
0.341450263648464, -0.273160210918771, -0.682900527296927, -0.0682900527296927, 
0.75119058002662, -0.887770685486005, -0.478030369107849, -0.273160210918771, 
-0.478030369107849, -0.478030369107849, 0.136580105459385, -0.478030369107849, 
0.136580105459385, -0.478030369107849, 0.136580105459385, -0.0682900527296927, 
-0.273160210918771, 0.136580105459385, 0.341450263648464, -0.478030369107849, 
0.75119058002662, 0.136580105459385, 0.341450263648464, 0.546320421837542, 
-0.887770685486005, 0.75119058002662, 0.341450263648464, -0.0682900527296927, 
-0.478030369107849, 0.546320421837542, 0.136580105459385, -0.682900527296927, 
-0.0682900527296927, 0.341450263648464, -0.478030369107849, -0.0682900527296927, 
-0.478030369107849, -0.0682900527296927, 0.341450263648464, -0.478030369107849, 
-0.682900527296927, 0.75119058002662, -0.478030369107849, -0.682900527296927, 
0.341450263648464, -0.887770685486005, -0.478030369107849, 0.546320421837542, 
-0.887770685486005, -0.478030369107849, -0.478030369107849, 0.341450263648464, 
0.75119058002662, -0.682900527296927, 0.75119058002662, 0.75119058002662, 
0.341450263648464, -0.0682900527296927, 0.546320421837542, -0.0682900527296927, 
0.136580105459385, 0.136580105459385, 0.136580105459385, 0.136580105459385, 
0.546320421837542, 0.546320421837542, -0.0682900527296927, 0.75119058002662, 
-0.0682900527296927, -0.0682900527296927, -0.682900527296927, 
-0.273160210918771, -0.682900527296927, -0.478030369107849, 0.136580105459385, 
0.75119058002662, 0.546320421837542, 0.341450263648464, -0.887770685486005, 
-0.0682900527296927, 0.136580105459385, 0.75119058002662, -0.273160210918771, 
-0.682900527296927, 0.136580105459385, -0.478030369107849, -0.273160210918771, 
-0.273160210918771, 0.136580105459385, 0.341450263648464, -0.478030369107849, 
-0.0682900527296927, -0.682900527296927, 0.75119058002662, -0.273160210918771, 
-0.478030369107849, -0.0682900527296927, -0.0682900527296927, 
-0.273160210918771, -0.0682900527296927, -0.478030369107849, 
0.75119058002662, -0.0682900527296927, 0.136580105459385, 0.546320421837542, 
0.546320421837542, -0.478030369107849, -0.273160210918771, 0.546320421837542, 
-0.478030369107849, -0.682900527296927, 0.75119058002662, -0.0682900527296927, 
-0.682900527296927, -0.682900527296927, 0.75119058002662, 0.341450263648464, 
-0.478030369107849, 0.75119058002662, 0.136580105459385, -0.887770685486005, 
0.341450263648464, 0.341450263648464, 0.546320421837542, -0.273160210918771, 
0.136580105459385, 0.75119058002662, -0.0682900527296927, -0.682900527296927, 
-0.478030369107849, -0.478030369107849, 0.75119058002662, 0.546320421837542, 
-0.478030369107849, 0.546320421837542, 0.136580105459385, -0.887770685486005, 
0.75119058002662, -0.0682900527296927, 0.75119058002662, 0.75119058002662, 
-0.273160210918771, -0.682900527296927, 0.546320421837542, 0.546320421837542, 
-0.887770685486005, 0.75119058002662, -0.273160210918771, 0.546320421837542, 
-0.0682900527296927, 0.136580105459385, 0.341450263648464, -0.478030369107849, 
0.136580105459385, 0.136580105459385, -0.273160210918771, 0.546320421837542, 
-0.273160210918771, -0.273160210918771, -0.273160210918771, 0.75119058002662, 
-0.887770685486005, -0.887770685486005, -0.0682900527296927, 
-0.478030369107849, -0.0682900527296927, 0.75119058002662, -0.273160210918771, 
0.136580105459385, -0.478030369107849, -0.273160210918771, 0.136580105459385, 
0.75119058002662, 0.546320421837542, -0.478030369107849, -0.273160210918771, 
-0.273160210918771, 0.136580105459385, -0.273160210918771, -0.0682900527296927, 
0.75119058002662, 0.136580105459385), resp.var = c(2L, 1L, 0L, 
1L, 0L, 0L, 0L, 0L, 0L, 1L, 3L, 1L, 0L, 1L, 0L, 1L, 2L, 0L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 2L, 
0L, 3L, 2L, 0L, 2L, 2L, 0L, 0L, 0L, 1L, 1L, 3L, 1L, 2L, 0L, 1L, 
0L, 0L, 1L, 0L, 2L, 0L, 2L, 4L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 
3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 
0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 2L, 0L, 1L, 0L, 4L, 1L, 0L, 
1L, 1L, 0L, 0L, 0L, 1L, 3L, 0L, 2L, 0L, 0L, 2L, 1L, 0L, 0L, 2L, 
0L, 0L, 0L, 2L, 0L, 0L, 3L, 0L, 0L, 2L, 1L, 1L, 0L, 0L, 3L, 1L, 
1L, 2L, 0L, 2L, 0L, 2L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 2L, 2L, 1L, 0L, 0L, 1L, 
0L, 0L, 0L, 0L, 6L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 
1L, 0L, 0L, 1L, 3L, 1L, 0L, 2L, 3L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 
0L, 0L, 0L, 0L, 1L, 2L, 1L, 1L, 0L, 0L, 2L, 0L, 2L, 0L, 0L, 1L, 
1L, 0L, 0L, 2L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
0L, 1L, 0L, 2L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 
0L, 3L, 0L, 0L, 3L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 2L, 1L, 1L, 0L, 2L, 2L, 0L, 2L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 
3L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 2L, 0L, 1L, 1L, 0L, 1L, 
0L, 3L, 1L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 0L, 
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  • pas trop dur en combinant predict avec ggplot ou lattice::xyplot. Reproductible exemple s'il vous plaît?
InformationsquelleAutor Jota | 2012-05-05
  1. 14

    Voici une réponse de toutes sortes (en passant, vous avez eu, il manque des guillemets dans votre bloc de données ci-dessus, qui ont du être corrigés manuellement ...)

    L'ajustement du modèle:

    library(lme4)
fit <- glmer(resp.var ~ cont.var:cat.var + (1|rand.eff) ,
data = sample.data , poisson)

    (Notez que c'est un peu bizarre de spécification de modèle -- les forces de toutes les catégories ont la même valeur en cont.var==0. Vouliez-vous dire cont.var*cat.var?

    library(ggplot2)
theme_update(theme_bw())  ## set white rather than gray background

    Rapide et sale régressions linéaires:

    ggplot(sample.data,aes(cont.var,resp.var,linetype=cat.var))+
geom_smooth(method="lm",se=FALSE)

    Maintenant avec une loi de Poisson GLM (mais ne tenant pas compte de l'effet aléatoire), et en montrant les points de données:

    ggplot(sample.data,aes(cont.var,resp.var,colour=cat.var))+
stat_sum(aes(size=..n..),alpha=0.5)+
geom_smooth(method="glm",family="poisson")

    Le prochain bit exige le développement (r-forge) version de lme4, qui a un predict méthode:

    Configurer le bloc de données à des fins de prédiction:

    predframe <- with(sample.data,
expand.grid(cat.var=levels(cat.var),
cont.var=seq(min(cont.var),
max(cont.var),length=51)))

    Prévoir au niveau de la population (REform=NA), sur le prédicteur linéaire (logit) échelle (c'est la seule façon, vous obtiendrez des lignes droites sur la parcelle)

    predframe$pred.logit <- predict(fit,newdata=predframe,REform=NA)
minmaxvals <- range(sample.data$cont.var)
ggplot(predframe,aes(cont.var,pred.logit,linetype=cat.var))+geom_line()+
geom_point(data=subset(predframe,cont.var %in% minmaxvals),
aes(shape=cat.var))

    R: diagramme des Interactions avec une constante et une variable catégorique pour un GLMM (lme4)
    Maintenant, sur la réponse de l'échelle:

    predframe$pred <- predict(fit,newdata=predframe,REform=NA,type="response")
ggplot(predframe,aes(cont.var,pred,linetype=cat.var))+geom_line()+
geom_point(data=subset(predframe,cont.var %in% minmaxvals),
aes(shape=cat.var))

    R: diagramme des Interactions avec une constante et une variable catégorique pour un GLMM (lme4)

    • Merci! Désolé de l'absence de guillemets. J'ai utilisé dput, et de ne pas modifier la sortie, à condition que l'échantillon de données. Je devrais avoir fait quelque chose d'autre. Et oui, vous avez raison, je devrais avoir utilisé * au lieu de : dans la spécification du modèle.
    • probablement juste une sorte de cut & coller l'erreur ... ?
    • Tout est un peu confus, mais je voudrais essayer install.packages("lme4",repos="http://lme4.r-forge.r-project.org/repos") -- le construire sur le principal r-forge référentiel est brisé à l'instant (vous pouvez toujours re-installer à partir de CRAN si nécessaire).
    • Rien mais Excel 🙂 : quels sont les symptômes de votre incapacité à s'installer? (De l'OS et des R version utilisez-vous?)
    InformationsquelleAutor Ben Bolker
  2. 3

    La jtools paquet (CRAN lien) peut faire le tracé de ce genre de modèle assez simple. Je suis le développeur de ce package.

    Nous l'ajustement du modèle comme Ben l'a fait dans sa réponse:

    library(lme4)
fit <- glmer(resp.var ~ cont.var:cat.var + (1 | rand.eff),
data = sample.data, family = poisson)

    Et avec jtools nous suffit d'utiliser la interact_plot fonction comme ceci:

    library(jtools)
interact_plot(fit, pred = cont.var, modx = cat.var)

    Le résultat:

    R: diagramme des Interactions avec une constante et une variable catégorique pour un GLMM (lme4)

    Par défaut des parcelles de la réponse de l'échelle, mais vous pouvez l'avoir tracée sur l'échelle linéaire avec la outcome.scale = "link" argument (la valeur par défaut est "response").

    InformationsquelleAutor commscho
  3. 1

    La effets package de support pour les lme4 modèles, et devrait être en mesure de faire ce que vous voulez.

    effets: Effet Affiche Linéaire, Linéaire Généralisé, et d'Autres Modèles

    Graphique et tabulaire, effet affiche, par exemple, des interactions, pour les différents modèles statistiques avec les prédicteurs linéaires.

    Il est également livré avec deux légèrement dépassées papiers (vous pouvez y penser comme des vignettes).

    InformationsquelleAutor landroni