extractSamples {BayesX} | R Documentation |

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.

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

`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. |

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 |

`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.

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.

Daniel Sabanes Bove, with contributions by Fabian Scheipl

## 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")

[Package *BayesX* version 0.3-1 Index]