plot_bsgw: Plot diagnostics for a bsgw object

In BSGW: Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression

Plot diagnostics for a bsgw object

Description

Four sets of MCMC diagnostic plots are currently generated: 1) log-likelihood and log-posterior (including shrinkage effect) as a function of iteration number, 2) coefficient trace plots, 3) coefficient autocorrelation plots, 4) coefficient histograms.

Usage

## S3 method for class 'bsgw'
plot(x, pval=0.05, burnin=round(x$control$iter/2), nrow=2, ncol=3, ...)

Arguments

x	A bsgw object, typically the output of bsgw function.
pval	The P-value at which lower/upper bounds on coefficients are calculated and overlaid on trace plots and historgrams.
burnin	Number of samples discarded from the beginning of an MCMC chain, after which parameter quantiles are calculated.
nrow	Number of rows of subplots within each figure, applied to plot sets 2-4.
ncol	Number of columns of subplots within each figure, applied to plot sets 2-4.
...	Further arguments to be passed to/from other methods.

Author(s)

Alireza S. Mahani, Mansour T.A. Sharabiani

Examples

library("survival")
data(ovarian)
est <- bsgw(Surv(futime, fustat) ~ ecog.ps + rx, ovarian
            , control=bsgw.control(iter=400, nskip=100))
plot(est)

BSGW documentation built on Dec. 28, 2022, 2:58 a.m.