stanoptimis: Optimization and importance sampling for Stan models.

Description Usage Arguments Value Examples

View source: R/stanoptimis.R

Optimize / importance sample a stan or ctStan model.

stanoptimis(standata, sm, init = "random", initsd = 0.01,
  sampleinit = NA, deoptim = FALSE, estonly = FALSE, tol = 1e-14,
  decontrol = list(), stochastic = FALSE, plotsgd = FALSE,
  is = TRUE, isloopsize = 1000, finishsamples = 500, tdf = 2,
  chancethreshold = 100, finishmultiply = 5, verbose = 0,
  cores = 2)

`standata`	list object conforming to rstan data standards.
`sm`	compiled stan model object.
`init`	vector of unconstrained parameter values, or character string 'random' to initialise with random values very close to zero.
`initsd`	positive numeric specifying sd of normal distribution governing random sample of init parameters, if init='random' .
`sampleinit`	either NA, or an niterations * nparams matrix of samples to initialise importance sampling.
`deoptim`	Do first pass optimization using differential evolution? Slower, but better for cases with multiple minima / difficult optimization.
`estonly`	if TRUE,just return point estimates under $rawest subobject.
`tol`	objective tolerance.
`decontrol`	List of control parameters for differential evolution step, to pass to `DEoptim.control`.
`stochastic`	Logical. Use stochastic gradient descent instead of mize (bfgs) optimizer. Still experimental, worth trying for either robustness checks or problematic, high dimensional, nonlinear, problems.
`plotsgd`	Logical. If TRUE, plot iteration details when using stochastic optimizer.
`is`	Logical. Use importance sampling, or just return map estimates?
`isloopsize`	Number of samples of approximating distribution per iteration of importance sampling.
`finishsamples`	Number of samples to draw for final results of importance sampling.
`tdf`	degrees of freedom of multivariate t distribution. Higher (more normal) generally gives more efficent importance sampling, at risk of truncating tails.
`chancethreshold`	drop iterations of importance sampling where any samples are chancethreshold times more likely to be drawn than expected.
`verbose`	Integer from 0 to 2. Higher values print more information during model fit – for debugging.
`cores`	Number of cpu cores to use.

list containing fit elements

#' \donttest{

library(rstan)
scode <- "
parameters {
  real y[2];
}
model {
  y[1] ~ normal(0, 1);
  y[2] ~ double_exponential(0, 2);
}
"

sm <- stan_model(model_code=scode)
fit <- sampling(sm, iter = 10000)
summary(fit)$summary

## extract samples as a list of arrays
e <- extract(fit, permuted = TRUE)

#for ml or map estimates
optimis <- stanoptimis(standata = list(),sm = sm,finishsamples = 3000,cores=2)
optimis$optimfit

#for posterior distributions
optimis <- stanoptimis(standata = list(),sm = sm,finishsamples = 3000,cores=6,tdf=2)

apply(optimis$rawposterior,2,mean)
apply(optimis$rawposterior,2,sd)
isdiag(optimis)

plot(density(optimis$rawposterior[,2],bw=.05))
points(density(e$y[,2],bw=.05),type='l',col=2)
}