BayesX (version 0.3-3)

extractSamples: Extract MCMC samples from a BayesX results directory

Description

This is a convenience function to extract samples from a BayesX results directory, which processes the log file to e.g. convert the spline coefficients samples to function values samples.

Usage

extractSamples(directoryWithBasename, 
               logfile = file.path(dirname(directoryWithBasename), "log.txt"))

Value

Returns a list with the extracted samples of effects and deviances as well as the prediction data.frame:

<function name>

for P-Splines, Random Walks and spatial effects: a list with mcmc objects 'functionSamples' and 'varianceSamples' containing the respective effects/function and variance parameter samples.

FixedEffects

an mcmc object of all fixed simple parametric effects

RandomEffects

if there is at least one random effect in the model, this is a list, with elements in the first hierarchy being the group ID names, and elements in the second hierarchy being the names of the covariates. The leafs are the mcmc objects 'functionSamples' and 'varianceSamples', as for the other non-fixed terms

Deviance

an mcmc object with the (unstandardized and saturated) deviance

means

if the option predictmu was used, this mcmc object contains the mean samples

scale

an mcmc object with the possible scale parameter samples

lassoCoefficients

an mcmc object with the possible lasso regression parameter samples

ridgeCoefficients

an mcmc object with the possible ridge regression parameter samples

PredictMeans

data.frame corresponding to the possible predictmean file in the BayesX directory

Additionally, entries for possibly remaining lasso or ridge variance parameters etc. are included in the return list.

Arguments

directoryWithBasename

The BayesX results directory with basename for the files (e.g. "results/test", if this was specified as outfile in BayesX for the bayesreg object)

logfile

The log file of the MCMC run, defaults to log.txt in the results directory.

Author

Daniel Sabanes Bove, with contributions by Fabian Scheipl

Warning

You should be sure that only one MCMC run is saved in the given results directory in order to get sensible results out of this function.

Examples

## get the samples
samples <- extractSamples(file.path(system.file("examples/samples", package="BayesX"),
                                     "res"))
str(samples)

## check deviance convergence
plot(samples$Deviance)

## fixed parametric effects
plot(samples$FixedEffects)

## nonparametric effects:

## handy plot function to get means and pointwise credible intervals
nonpPlot <- function(samplesMatrix,
                     ...)
{
    x <- as.numeric(colnames(samplesMatrix))

    yMeans <- colMeans(samplesMatrix)
    yCredible <- t(apply(samplesMatrix,
                         MARGIN=2,
                         FUN=quantile,
                         prob=c(0.025, 0.975),
                         na.rm=TRUE))
    
    matplot(x, cbind(yMeans, yCredible),
            type="l",
            lty=c(1, 2, 2),
            lwd=c(2, 1, 1),
            col=c(1, 2, 2),
            ...)
}

nonpPlot(samples$f_x1$functionSamples,
         xlab=expression(x[1]),
         ylab=expression(hat(f)(x[1])))
nonpPlot(samples$f_x2$functionSamples,
         xlab=expression(x[2]),
         ylab=expression(hat(f)(x[2])))

## spatial effect
tanzania <- read.bnd(file=system.file("examples/tanzania.bnd", package="BayesX"))
drawmap(map=tanzania,
        data=
        with(samples$f_district,
             data.frame(name=colnames(functionSamples),
                        estimate=colMeans(functionSamples))),
        regionvar="name",
        plotvar="estimate")