Bayesian Survival Regression with Flexible Error and Random Effects Distributions


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Documentation for package ‘bayesSurv’ version 3.3

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bayesBisurvreg Population-averaged accelerated failure time model for bivariate, possibly doubly-interval-censored data. The error distribution is expressed as a~penalized bivariate normal mixture with high number of components (bivariate G-spline).
bayesDensity Summary for the density estimate based on the mixture Bayesian AFT model.
bayesGspline Summary for the density estimate based on the model with Bayesian G-splines.
bayesHistogram Smoothing of a uni- or bivariate histogram using Bayesian G-splines
bayessurvreg1 A Bayesian survival regression with an error distribution expressed as a~normal mixture with unknown number of components
bayessurvreg1.files2init Read the initial values for the Bayesian survival regression model to the list.
bayessurvreg2 Cluster-specific accelerated failure time model for multivariate, possibly doubly-interval-censored data. The error distribution is expressed as a~penalized univariate normal mixture with high number of components (G-spline). The distribution of the vector of random effects is multivariate normal.
bayessurvreg3 Cluster-specific accelerated failure time model for multivariate, possibly doubly-interval-censored data with flexibly specified random effects and/or error distribution.
bayessurvreg3Para Cluster-specific accelerated failure time model for multivariate, possibly doubly-interval-censored data with flexibly specified random effects and/or error distribution.
cgd Chronic Granulomatous Disease data
credible.region Compute a simultaneous credible region (rectangle) from a sample for a vector valued parameter.
C_bayesBisurvreg Population-averaged accelerated failure time model for bivariate, possibly doubly-interval-censored data. The error distribution is expressed as a~penalized bivariate normal mixture with high number of components (bivariate G-spline).
C_bayesDensity Summary for the density estimate based on the mixture Bayesian AFT model.
C_bayesGspline Summary for the density estimate based on the model with Bayesian G-splines.
C_bayesHistogram Smoothing of a uni- or bivariate histogram using Bayesian G-splines
C_bayessurvreg1 A Bayesian survival regression with an error distribution expressed as a~normal mixture with unknown number of components
C_bayessurvreg2 Cluster-specific accelerated failure time model for multivariate, possibly doubly-interval-censored data. The error distribution is expressed as a~penalized univariate normal mixture with high number of components (G-spline). The distribution of the vector of random effects is multivariate normal.
C_cholesky A Bayesian survival regression with an error distribution expressed as a~normal mixture with unknown number of components
C_iPML_misclass_GJK Cluster-specific accelerated failure time model for multivariate, possibly doubly-interval-censored data with flexibly specified random effects and/or error distribution.
C_marginal_bayesGspline Summary for the marginal density estimates based on the bivariate model with Bayesian G-splines.
C_predictive Compute predictive quantities based on a Bayesian survival regression model fitted using bayessurvreg1 function.
C_predictive_GS Compute predictive quantities based on a Bayesian survival regression model fitted using bayesBisurvreg or bayessurvreg2 or bayessurvreg3 functions.
C_rmvnormR2006 Sample from the multivariate normal distribution
C_rwishartR3 Sample from the Wishart distribution
C_sampledKendallTau Estimate of the Kendall's tau from the bivariate model
densplot2 Probability density function estimate from MCMC output
files2coda Read the sampled values from the Bayesian survival regression model to a coda mcmc object.
give.summary Brief summary for the chain(s) obtained using the MCMC.
marginal.bayesGspline Summary for the marginal density estimates based on the bivariate model with Bayesian G-splines.
plot.bayesDensity Plot an object of class bayesDensity
plot.bayesGspline Plot an object of class bayesGspline
plot.marginal.bayesGspline Plot an object of class marginal.bayesGspline
predictive Compute predictive quantities based on a Bayesian survival regression model fitted using bayessurvreg1 function.
predictive.control Compute predictive quantities based on a Bayesian survival regression model fitted using bayessurvreg1 function.
predictive2 Compute predictive quantities based on a Bayesian survival regression model fitted using bayesBisurvreg or bayessurvreg2 or bayessurvreg3 functions.
predictive2.control Compute predictive quantities based on a Bayesian survival regression model fitted using bayesBisurvreg or bayessurvreg2 or bayessurvreg3 functions.
predictive2Para Compute predictive quantities based on a Bayesian survival regression model fitted using bayesBisurvreg or bayessurvreg2 or bayessurvreg3 functions.
print.bayesDensity Print a summary for the density estimate based on the Bayesian model.
print.simult.pvalue Compute a simultaneous p-value from a sample for a vector valued parameter.
rMVNorm Sample from the multivariate normal distribution
rWishart Sample from the Wishart distribution
sampleCovMat Compute a sample covariance matrix.
sampled.kendall.tau Estimate of the Kendall's tau from the bivariate model
scanFN Read Data Values
simult.pvalue Compute a simultaneous p-value from a sample for a vector valued parameter.
tandmob2 Signal Tandmobiel data, version 2
tandmobRoos Signal Tandmobiel data, version Roos
traceplot2 Trace plot of MCMC output.
vecr2matr Transform single component indeces to double component indeces