resample: Resample map2stan fit

In joepowers16/rethinking: Statistical Rethinking book package

Description

Sample from a new chain or chains, using a previous map2stan fit object.

Usage

resample( object , iter=1e4 , warmup=1000 , chains=1 , cores=1 , 
    DIC=TRUE , WAIC=TRUE , rng_seed , data , ... )

Arguments

object	Object of class map2stan
iter	Number of sampling iterations, including warmup
warmup	Number of adaptation steps
chains	Number of independent chains
cores	Number of cores to distribute chains across
DIC	If TRUE, computes DIC after sampling
WAIC	If TRUE, computes WAIC after sampling
rng_seed	Optional seed to use for all chains. When missing, a random seed is chosen and used for all chains.
...	Other parameters to pass to stan

Details

This function is a convenience for drawing more samples from an initial map2stan fit.

When cores is set greater than 1, either mclapply (on a unix system) or parLapply (on a Windows system) is used to run the chains, distributing them across processor cores. The results are automatically recombined with sflist2stanfit.

Value

An object of class map2stan, holding the new samples, as well as all of the original formulas and data for the model.

Author(s)

Richard McElreath

See Also

map2stan, mclapply, sflist2stanfit

Examples

## Not run: 
data(Trolley)
d <- Trolley
d2 <- list(
    y=d$response,
    xA=d$action,
    xI=d$intention,
    xC=d$contact,
    id=as.integer(d$id)
)
Nid <- length(unique(d2$id))

# ordered logit regression with varying intercepts
m.init <- map2stan(
    alist(
        y ~ dordlogit( phi , cutpoints ),
        phi <- aj + bA*xA + bI*xI + bC*xC,
        c(bA,bI,bC) ~ dnorm(0,1),
        aj[id] ~ dnorm(0,sigma_id),
        sigma_id ~ dcauchy(0,2.5),
        cutpoints ~ dcauchy(0,2.5)
    ),
    data=d2 ,
    start=list(
        bA=0,bI=0,bC=0,
        cutpoints=c(-2,-1.7,-1,-0.2,0.5,1.3),
        aj=rep(0,Nid),sigma_id=1
    ),
    types=list(cutpoints="ordered") , 
    iter=2
)

# Note: parallel chains won't work on Windows
m <- resample( m.init , chains=3 , cores=3 , warmup=1000 , iter=3000 )

## End(Not run)

joepowers16/rethinking documentation built on June 2, 2019, 6:52 p.m.