Using sims from predictInterval to plot intervals around continuous covariate

gistfile1.txt

library(lmerTest)
library(merTools)
library(tidyverse)



# linear model
m <- lmer(Reaction~Days*I(Days^2)+(1|Subject), data=sleepstudy)
newdata = expand.grid(Reaction=NA, Days=1:5, Subject=Inf) %>% as.data.frame
sims <- predictInterval(m, newdata=newdata, returnSims = T)
bind_sims(attr(sims, 'sim.results'), newdata) %>% 
  ggplot(aes(Days, fitted)) + geom_smooth(method="loess")



# logit model

require("HSAUR2")


# run a raw plot first to sanity check against
rawplot <- toenail %>% 
  ggplot(aes(visit, as.numeric(outcome=="moderate or severe"), group=treatment, color=treatment)) +
  geom_smooth()
rawplot

m2 <- glmer(outcome~treatment*visit+(1|patientID),
             data=toenail,
             family=binomial)

toenail %>% glimpse
newdata2 = expand.grid(outcome=NA, treatment=c("terbinafine", "itraconazole"), patientID=Inf, visit=seq(1,5, by=.25))

sims2 <- predictInterval(m2, newdata=newdata2, returnSims = T)
modelplot <- bind_sims(attr(sims2, 'sim.results'), newdata2) %>% 
  ggplot(aes(visit, exp(fitted), group=treatment, color=forcats::fct_rev(treatment))) + geom_smooth()
modelplot

# now can calulate and plot some other quantity - eg difference between treatments
diffplot <- bind_sims(attr(sims2, 'sim.results'), newdata2) %>% 
  dcast(sim+visit~treatment) %>% 
  mutate(diff=exp(terbinafine - itraconazole)) %>% 
  ggplot(aes(visit, diff)) + geom_smooth()
diffplot



# combine raw, model predicted group, and difference plots
grid.arrange(
rawplot + theme(legend.position="bottom"),
modelplot + theme(legend.position="bottom"),
diffplot +theme(legend.position="bottom"), 
nrow=3)