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Relabelling helper function for working with factors in rstan

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
30/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
r
Domain
tooling

Research direction

No repository files or tests are named. Start by reviewing how rstantools handles rstan fit objects and parameter names, then compare the proposed delabel and relabel examples with existing interfaces. Done would require an agreed general-purpose helper design and corresponding validation, but the issue does not define acceptance criteria.

Written by the indexing model from the issue text.

Description

Summary:

When using factor variables (which are have attached meaningful level names), would it be useful/possible to have a function that relabels variables in the Stan fit object with the relevant factor levels?

Description:

When using categorical variables in stan you have to drop the level labels. This is fine and sensible, but with variables that have a number of levels, the output can be difficult to read. Would it be possible to write a helper function that relabels the numeric index with the relevant label for easier use? @jgabry thought it might be, but wasn't sure if it would be too complex to create something that was useful in the more general.

Reproducible Steps:

For example. With the 8 schools model the schools are coded as numbers. However, in real world data, they would often be recorded with meaningful names (here we use states).

schools_df <- data.frame(schools = state.name[1:8],
y = c(28,  8, -3,  7, -1,  1, 18, 12),
sigma = c(15, 10, 16, 11,  9, 11, 10, 18))
schools_df$schools = as.factor(schools_df$schools)

I am proposing a pair of functions that force the factors to numeric for use in rstan, and then relabels the relevant variables in the stan fit object. e.g. for the simple 8 schools case.

delabel <- function(x){
  return(as.numeric(x))
}

relabel <- function(x,stan_fit,variables){
   current_names <- names(stan_fit)
   match <- cbind(levels(x),levels(as.factor(as.numeric(x))))
   for (i in 1:length(variables)){
     loc_var <- grepl(paste0("^",variables[i]),current_names)
     for(j in 1:nrow(match)){
       current_names[loc_var] <- gsub(pattern = match[j,2], replacement = match[j,1],current_names[loc_var])
     }
   }
   names(stan_fit) <- current_names
   return(stan_fit)
}

The idea is that is would work something like this:

schools_dat <- list(J = 8, 
                    school = delabel(schools_df$schools),
                    y = schools_df$y,
                    sigma =schools_df$sigma)

library(rstan)

fit <- stan(file = '8schools.stan', data = schools_dat)

fit2 <- relabel(schools_df$schools,fit,c('theta','eta'))

colnames(as.matrix(fit2))
library(bayesplot)
mcmc_areas_ridges(as.matrix(fit2, pars = "theta"))
Dominant language
R
Stars
48
Forks
24
Avg merge
1h 17m
Merged PRs (30d)
1

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