summary.basta: Summarizing and plotting Bayesian Survival Trajectory Analysis (BaSTA) model outputs.

Description

These functions are all generic methods for class basta.

Usage

## S3 method for class 'basta':
summary(object, \dots)
## S3 method for class 'basta':
print(x, \dots)
## S3 method for class 'basta':
plot(x, plot.trace = TRUE, trace.name = "theta", \dots)

Arguments

object

An object of class basta.

plot.trace

A logical argument. If TRUE the raw parameter traces are plotted, else, the predictive intervals for the resulting survival probability and mortality rates are plotted.

trace.name

Character string indicating the set of parameters or posteriors to be plotted. The options are: "theta" to plot the survival model parameters; "gamma" to plot the proportional hazards parameters (if it applies, else plot.ba

...

Additional arguments passed to functions print, summary and plot.

Details

For objects of class basta, the print function returns three summary elements describing the model and its results, namely: call, run and coefficients. call describes the basic model used (i.e. exponential, Gompertz, Weibull or logistic), the shape chosen, simple, Makeham or bathtub, the covariate structure chosen, fused, prop.haz or all.in.mort and which covariates (if any) were categorical and which continuous.

The summary element run describes whether all of the simulations ran for all of the iterations (specified by niter) specified by the user in the basta function. If not all of the runs were completed, it outlines which of them have failed.

The summary element coefficients prints out the estimated coefficients for all parameters in the model, as well as their standard errors and 95% upper and lower credible intervals. It also includes a measure of serial autocorrelation for each parameter calculated from the thinned parameter chains, an update rate per parameter (commonly the same for all survival and proportional hazards parameters), and the potential scale reduction factor for each parameter as a measure of convergence (Gelman et al. 2004).

Function summary includes all the previous elements, as well as a summary description of the priors and jump standard deviations for all survival parameters, a calibration of the Kullback-Leibler discrepancy as a measure of parameter similarities for those parameters associated to categorical covariates (McCulloch 1989), and a measure of model fit based on the deviance information criterion (DIC) (Spiegelhalter et al. 2002).

Function plot takes objects of class basta to create trace plots or, if the argument for plot.trace is set to FALSE, it plots estimated survival probabilities and mortality rates with their 95% predictive intervals.

References

Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B. (2004) Bayesian data analysis. 2nd edn. Chapman & Hall/CRC, Boca Raton, Florida, USA.

McCulloch, R.E. (1989) Local model influence. Journal of the American Statistical Association, 84, 473-478.

Spiegelhalter, D.J., Best, N.G., Carlin, B.P. and Van Der Linde, A. (2002) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B 64, 583-639.

Examples

Run this code

## Load BaSTA output:
data("sim1Out", package = "BaSTA")

## Print summary output:
summary(sim1Out)

## Plot traces for mortality parameters (theta):
plot(sim1Out)

## Plot traces for proportional hazards parameters (gamma):
plot(sim1Out, trace.name = "gamma")

## Plot predicted mortality and survival:
plot(sim1Out, plot.trace = FALSE)