rethinking (version 1.35)

resample: Resample map2stan fit

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 , ... )

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

...

Other parameters to pass to stan

Value

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

Details

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

When cores is set greater than 1, mclapply is used to run the chains, distributing them across the cores. The results are automatically combined with sflist2stanfit.

References

McElreath 2011, Statistical Rethinking.

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(
    list(
        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[6]") , 
    iter=200
)

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

# }