simsurv: simsurv function

In bentaylor1/spatsurv: Bayesian Spatial Survival Analysis with Parametric Proportional Hazards Models

Description

A function to simulate spatial parametric proportional hazards model. The function works by simulating candidate survival times using MCMC in parallel for each individual based on each individual's covariates and the common parameter effects, beta.

Usage

simsurv(X = cbind(age = runif(100, 5, 50), sex = rbinom(100, 1, 0.5), cancer =
  rbinom(100, 1, 0.2)), beta = c(0.0296, 0.0261, 0.035), omega = 1,
  dist = exponentialHaz(), coords = matrix(runif(2 * nrow(X)), nrow(X), 2),
  cov.parameters = c(1, 0.1), cov.model = covmodel(model = "exponential",
  pars = NULL), mcmc.control = mcmcpars(nits = 1e+05, burn = 10000, thin =
  90), savechains = TRUE)

Arguments

X	a matrix of covariate information
beta	the parameter effects
omega	vector of parameters for the baseline hazard model
dist	the distribution choice: exp or weibull at present
coords	matrix with 2 columns giving the coordinates at which to simulate data
cov.parameters	a vector: the parameters for the covariance function
cov.model	an object of class covmodel, see ?covmodel
mcmc.control	mcmc control paramters, see ?mcmcpars
savechains	save all chains? runs faster if set to FALSE, but then you'll be unable to conduct convergence/mixing diagnostics

Value

in list element 'survtimes', a vector of simulated survival times (the last simulated value from the MCMC chains) in list element 'T' the MCMC chains

See Also

covmodel, survspat, tpowHaz, exponentialHaz, gompertzHaz, makehamHaz, weibullHaz

bentaylor1/spatsurv documentation built on May 12, 2019, 3:02 p.m.