Plot poisson mixed models with ggplot2 - r

Question

I try to make a plot for standard purposes with zero inflated model and zero inflated mixed model using ggplot2 without success. For this, I try:
#Packages
library(pscl)
library(glmmTMB)
library(ggplot2)
library(gridExtra)
# Artificial data set
set.seed(007)
n <- 100 # number of subjects
K <- 8 # number of measurements per subject
t_max <- 5 # maximum follow-up time
DF <- data.frame(id = rep(seq_len(n), each = K),
time = c(replicate(n, c(0, sort(runif(K - 1, 0, t_max))))),
sex = rep(gl(2, n/2, labels = c("male", "female")), each = K))
DF$y <- rnbinom(n * K, size = 2, mu = exp(1.552966))
str(DF)
Using zero inflated poisson model with pscl package
time2<-(DF$time)^2
mZIP <- zeroinfl(y~time+time2+sex|time+sex, data=DF)
summary(mZIP)
If I imagine thal all coefficients are significant
# Y estimated
pred.data1 = data.frame(
time<-DF$time,
time2<-(DF$time)^2,
sex<-DF$sex)
pred.data1$y = predict(mZIP, newdata=pred.data1, type="response")
Now using zero inflated poisson mixed model with glmmTMB package
mZIPmix<- glmmTMB(y~time+time2+sex+(1|id),
data=DF, ziformula=~1,family=poisson)
summary(mZIPmix)
#
# new Y estimated
pred.data2 = data.frame(
time<-DF$time,
time2<-(DF$time)^2,
sex<-DF$sex,
id<-DF$id)
pred.data2$y = predict(mZIPmix, newdata=pred.data2, type="response")
Plot zero inflated poisson model and mixed poisson model
par(mfrow=c(1,2))
plot1<-ggplot(DF, aes(time, y, colour=sex)) +
labs(title="Zero inflated model") +
geom_point() +
geom_line(data=pred.data1) +
stat_smooth(method="glm", family=poisson(link="log"), formula = y~poly(x,2),fullrange=TRUE)
plot2<-ggplot(DF, aes(time, y, colour=sex)) +
labs(title="Zero inflated mixed model") +
geom_point() +
geom_line(data=pred.data2) +
stat_smooth(method="glm", family=poisson(link="log"), formula = y~poly(x,2),fullrange=TRUE)## here a don't find any method to mixed glm
grid.arrange(plot1, plot2, ncol=2)
#-
Doesn't work of sure. Is possible to make this using ggplot2?
Thanks in advance

Answer 1

I'm not sure, but it looks to me that you're looking for marginal effects. You can do this with the ggeffects-package. Here are two examples, using your simulated data, that create a ggplot-object, one with and one w/o raw data.
library(glmmTMB)
library(ggeffects)
mZIPmix<- glmmTMB(y~poly(time,2)+sex+(1|id), data=DF, ziformula=~1,family=poisson)
# compute marginal effects and create a plot.
# the tag "[all]" is useful for polynomial terms, to produce smoother plots
ggpredict(mZIPmix, c("time [all]", "sex")) %>% plot(rawdata = TRUE, jitter = .01)
ggpredict(mZIPmix, c("time [all]", "sex")) %>% plot(rawdata = FALSE)
Created on 2019-05-16 by the reprex package (v0.2.1)
Note that sex only has an "additive" effect. Maybe you want to model an intercation between time and sex?
mZIPmix<- glmmTMB(y~poly(time,2)*sex+(1|id), data=DF, ziformula=~1,family=poisson)
ggpredict(mZIPmix, c("time [all]", "sex")) %>% plot(rawdata = TRUE, jitter = .01)
ggpredict(mZIPmix, c("time [all]", "sex")) %>% plot()
Created on 2019-05-16 by the reprex package (v0.2.1)