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 = "exp", 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