https://github.com/kaskr/tmbstan

https://github.com/kaskr/tmbstan

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• Host: GitHub
• URL: https://github.com/kaskr/tmbstan
• Owner: kaskr
• Created: 2017-09-02T13:52:14.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2024-02-16T11:16:26.000Z (7 months ago)
• Last Synced: 2024-07-17T00:39:10.217Z (2 months ago)
• Language: R
• Size: 75.2 KB
• Stars: 30
• Watchers: 5
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
## Package tmbstan

[![Build Status](https://travis-ci.org/kaskr/tmbstan.svg?branch=master)](https://travis-ci.org/kaskr/tmbstan)

MCMC sampling from [TMB](https://github.com/kaskr/adcomp/wiki) (Template Model Builder) model object using Stan. See

Monnahan CC, Kristensen K (2018) No-U-turn sampling for fast Bayesian inference in ADMB and TMB: Introducing the adnuts and tmbstan R packages. PLoS ONE 13(5): e0197954. https://doi.org/10.1371/journal.pone.0197954

## Requirements

TMB version `>= 1.7.12` (on CRAN). Models must be re-compiled using

this new version.

## Install

```r

devtools::install_github("kaskr/tmbstan/tmbstan")

```

## Examples

```r

library(tmbstan)

runExample("simple")

## Run a single chain in serial with defaults

fit <- tmbstan(obj, chains=1)

## Run in parallel with a init function

cores <- parallel::detectCores()-1

options(mc.cores = cores)

init.fn <- function()

  list(u=rnorm(114), beta=rnorm(2), logsdu=runif(1,0,10), logsd0=runif(1,0,1))

fit <- tmbstan(obj, chains=cores, open_progress=FALSE, init=init.fn)

## To explore the fit use shinystan

library(shinystan)

launch_shinystan(fit)

## Can also get ESS and Rhat from rstan::monitor

mon <- monitor(fit)

max(mon$Rhat)

min(mon$Tail_ESS)

## Other methods provided by 'rstan'

class(fit)

methods(class="stanfit")

## Pairs plot of the fixed effects

pairs(fit, pars=names(obj$par))

## Trace plot

traceplot(fit, pars=names(obj$par), inc_warmup=TRUE)

## Can extract marginal posteriors easily

post <- as.matrix(fit)

hist(post[,'u[1]'])                     # random effect

hist(post[,'logsd0'])                   # fixed effect

## What if you want a posterior for derived quantities in the report? Just

## loop through each posterior sample (row) and call the report function

## which returns a list. The last column is the log-posterior density (lp__)

## and needs to be dropped

obj$report(post[1,-ncol(post)])         # sd0 is only element

sd0 <- rep(NA, len=nrow(post))

for(i in 1:nrow(post)){

  r <- obj$report(post[i,-ncol(post)])

  sd0[i] <- r$sd0

}

hist(sd0)

## It is also possible to use the Laplace approximation to integrate the

## random effects while using NUTS to integrate the fixed effects.

## ****This is generally not recommended****

init.fn <- function()

  list(beta=rnorm(2), logsdu=runif(1,0,10), logsd0=runif(1,0,1))

fit <- tmbstan(obj, chains=cores, open_progress=FALSE,

               init=init.fn, laplace=TRUE)

## There are no posterior samples for the random effects because they are

## integrated out by the LA. See Monnahan and Kristensen (2019) for discussion

## of why this would be worth doing. Typically it will be slower and less

## accurate than laplace=FALSE (the default).

names(as.data.frame(fit))

```