Is it possible to plot the random intercept or slope of a mixed model when it has more than one predictor?
With one predictor I would do like this:
#generate one response, two predictors and one factor (random effect)
resp<-runif(100,1, 100)
pred1<-c(resp[1:50]+rnorm(50, -10, 10),resp[1:50]+rnorm(50, 20, 5))
pred2<-resp+rnorm(100, -10, 10)
RF1<-gl(2, 50)
#gamm
library(mgcv)
mod<-gamm(resp ~ pred1, random=list(RF1=~1))
plot(pred1, resp, type="n")
for (i in ranef(mod$lme)[[1]]) {
abline(fixef(mod$lme)[1]+i, fixef(mod$lme)[2])
}
#lmer
library(lme4)
mod<-lmer(resp ~ pred1 + (1|RF1))
plot(pred1, resp, type="n")
for (i in ranef(mod)[[1]][,1]) {
abline(fixef(mod)[1]+i, fixef(mod)[2])
}
But what if I have a model like this instead?:
mod<-gamm(resp ~ pred1 + pred2, random=list(RF1=~1))
Or with lmer
mod<-lmer(resp ~ pred1 + pred2 + (1|RF1))
Should I consider all the coefficients or only the ones of the variable that I'm plotting?
Thanks
## generate one response, two predictors and one factor (random effect)
set.seed(101)
resp <- runif(100,1,100)
pred1<- rnorm(100,
mean=rep(resp[1:50],2)+rep(c(-10,20),each=50),
sd=rep(c(10,5),each=50))
pred2<- rnorm(100, resp-10, 10)
NOTE that you should probably not be trying to fit a random
effect for an grouping variable with only two levels -- this will
almost invariably result in an estimated random-effect variance of zero,
which will in turn put your predicted lines right on top of each
other -- I'm switching from gl(2,50)
to gl(10,10)
...
RF1<-gl(10,10)
d <- data.frame(resp,pred1,pred2,RF1)
#lmer
library(lme4)
mod <- lmer(resp ~ pred1 + pred2 + (1|RF1),data=d)
The development version of lme4
has a predict()
function
that makes this a little easier ...
pred1
with pred2
equal to its mean,
and vice versa. This is all a little bit cleverer than it needs
to be, since it generates all the values for both focal predictors
and plots them with ggplot in one go ...()
nd <- with(d,
rbind(data.frame(expand.grid(RF1=levels(RF1),
pred1=seq(min(pred1),max(pred1),length=51)),
pred2=mean(pred2),focus="pred1"),
data.frame(expand.grid(RF1=levels(RF1),
pred2=seq(min(pred2),max(pred2),length=51)),
pred1=mean(pred1),focus="pred2")))
nd$val <- with(nd,pred1[focus=="pred1"],pred2[focus=="pred2"])
pframe <- data.frame(nd,resp=predict(mod,newdata=nd))
library(ggplot2)
ggplot(pframe,aes(x=val,y=resp,colour=RF1))+geom_line()+
facet_wrap(~focus,scale="free")
pred1
and generating predictions for a (small/discrete) range of pred2
values ...()
nd <- with(d,
data.frame(expand.grid(RF1=levels(RF1),
pred1=seq(min(pred1),max(pred1),length=51),
pred2=seq(-20,100,by=40))))
pframe <- data.frame(nd,resp=predict(mod,newdata=nd))
ggplot(pframe,aes(x=pred1,y=resp,colour=RF1))+geom_line()+
facet_wrap(~pred2,nrow=1)
You might want to set scale="free"
in the last facet_wrap()
... or
use facet_grid(~pred2,labeller=label_both)
For presentation you might want to replace the colour
aesthetic,
with group
, if all you want to do is distinguish among groups
(i.e. plot separate lines) rather than identify them ...