Plotting estimate + raw data, zero inflated poisson

Interfaces brms
#1

Hi all,

To be able to modify the plot at will I want reproduce the plot obtained with:
plot(marginal_effects(MODEL_NAME), points=T)

As an example I used the data from
https://stats.idre.ucla.edu/r/dae/zip/
https://stats.idre.ucla.edu/stat/data/fish.csv

deleting a couple of extreme points for plot purposes:
fish<-read.csv(“https://stats.idre.ucla.edu/stat/data/fish.csv”)
fish$camper<-as.factor(fish$camper)
fish<-subset(fish, count!=149 & count!=65)

I ran the following ZERO-INFLATED model

mod_pois_zero = brms::brm(count ~ child + camper +(1| persons),
control = list(adapt_delta = 0.999, max_treedepth = 12),
iter = 6000,
cores = 4,
data = fish,
family = “zero_inflated_poisson”,
file=“mod_pois”)

I wrote the following script to plot estimate and raw data as for marginal_effects:

#extract posterior samples
d<-posterior_samples(mod_pois)

#select the variable(s) you want to plot
d_sub<-d[,c("b_Intercept", "b_child")]
#transform it to a matrix
d_sub1<-as.matrix(d_sub)

# to plot my simulation I have to extract the effect and add them to my original dataset
newdat<-data.frame(b_child = seq(-0,3, by = 0.025)) 
#if you choose the same length of your database you can add it easily
#put the entire range of your data

#create the matrix you are going to fill
Xmat <-model.matrix(~ b_child, data=newdat)
fitmat<-matrix(ncol=nrow(d), nrow=nrow(newdat))
#Fill the matrix
for(i in 1:nrow(d)) fitmat[,i] <- exp(Xmat%*%d_sub1[i,]) #poisson model
#for(i in 1:nrow(d)) fitmat[,i] <- Xmat%*%d_sub1[i,]      #gaussian model

#add the credible interval and fit
newdat$lower<- apply(fitmat,1,quantile, prob=0.025)
newdat$upper<-apply(fitmat, 1, quantile, prob=0.975)
newdat$fit<-apply(fitmat, 1, quantile, prob=0.5)

#newdat$fit<-exp(Xmat%*% fixef(mod_pois)) #fixef does not work in brms


#now you can either merge newdat with your original database or plot them directly

plot(fish$count ~fish$child,
     xlab =("b"), ylab = ("a"))
lines(newdat$b_child, newdat$fit, lwd=2)
lines(newdat$b_child, newdat$lower)
lines(newdat$b_child, newdat$upper)

the script works fine for norm and poisson models but the estimates with zero_inflated are wrong.

plot_brms

my_plot

How can I back-transform zero inflated models? clearly exp() is not enough.
Where can I find this information for the rest of the link functions ( e.g. skew normal, etc.)?

On a different note, the default plot plot(marginal_effects(MODEL), points=T) always cut the extreme points, any idea why?

Also if someone has a better strategy to plot raw data and estimates from brms I would be glad to know. Online I could not find any example that I was able to reproduce.

Thanks and best regards

#2

This seems like a brms-related question. I would add the brms tag to your post so that the right people see it (looks like a plot someone will have produced before).

#3

Thanks for the suggestion! I do not have the possibility to insert the brms tag, therefore I moved the question to a different section, is it what you meant?
Cheers and thanks

#4

You can use the fitted method to create mean predictions without having to hand code anything based on fixef or whatever (which is a suboptimal approach in many ways anyway).

1 Like
#5

Thank you for your reply Paul.
I will try to used fitted as suggested and give up on my approach.
Can you suggest on top of your mind blog and/or code to kick-start plotting raw data and linear models?

For categorical I used:
https://mjskay.github.io/tidybayes/articles/tidy-brms.html
For linear I found the following but I could not follow it:
http://rpubs.com/jwesner/gamma_glm

Much appreciated

#6

I wrote the following to plot from fitted as suggested, however, I do not obtain the same values of plot(marginal_effects(MODEL), points=T)

This is likely due to 2 reasons:

  1. I cannot include the contribute of random factors re_formula = NA (I could for this simple example but for a complicated model with multiple random factor I cannot achieve a single length for “newdata”)
  1. I have to set my variable “camper” as having a single value, if I set it as a factor the other variables will take one level at the time and the result is a plot not possible to visualize

Anyone experienced the same difficulties?
Thanks again for any reply

newdata = data.frame(child=seq(from=0, to=3,length.out=248),
                         camper=seq(from=0, to=0,length.out=1))
    posterior_fitted2 <- fitted(mod_pois_zero, newdata=newdata, re_formula = NA,summary=T)
    posterior_fitted_plot2 <- as_tibble(posterior_fitted2) %>% 
      mutate(child = newdata$child) #adds the predictor values from newdata


ggplot()+
  geom_line(data = posterior_fitted_plot2, aes(x=child,y=Estimate, ymax=Q97.5, ymin=Q2.5),size=1.2, color="dodgerblue")+
  geom_ribbon(data = posterior_fitted_plot2, aes(x=child,y=Estimate, ymax=Q97.5, ymin=Q2.5),fill="grey50",alpha=0.2)+
  geom_point(data = fish, aes(x=child,y=count))

fitted

#7

You may need to also set robust = TRUE in fitted.

1 Like
#8

Thank you Paul much appreciated.

I need also to add that I found that in most cases it is not even necessary to use fitted.
Thanks to the fantastic versatility of brms it is possible to extract plotting values from marginal_effects (independently of the model link function).
fitted can be useful when some more customization is necessary.
Thanks again, very neat package.

for example:

marg<-marginal_effects(YOUR_MODEL)

NEWDAT<-marg$YOUR_VARIABLE_OF_INTEREST

ggplot()+
  geom_line(data =  NEWDAT  , aes(x= YOUR_VARIABLE_OF_INTEREST  ,y=estimate__, ymax=upper__ , ymin=lower__),size=1.3, color="blue")+
  geom_ribbon(data =  NEWDAT  , aes(x= YOUR_VARIABLE_OF_INTEREST ,y=estimate__, ymax=upper__, ymin=lower__),fill="skyblue4",alpha=0.3)+
  geom_point(data = ORIGINAL_DATASET, aes(x=ORIGINAL_X.z,y=ORIGINAL_Y), 
             color="blue",alpha=0.08) + 
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"))