A function to run a Bayesian analysis on censored spatial survial data assuming a proportional hazards model using an adaptive Metropolis-adjusted Langevin algorithm.
survspat(
formula,
data,
dist,
cov.model,
mcmc.control,
priors,
shape = NULL,
ids = list(shpid = NULL, dataid = NULL),
control = inference.control(gridded = FALSE),
boundingbox = NULL
)an object inheriting class 'mcmcspatsurv' for which there exist methods for printing, summarising and making inference from.
the model formula in a format compatible with the function flexsurvreg from the flexsurv package
a SpatialPointsDataFrame object containing the survival data as one of the columns OR for polygonal data a data.frame, in which case, the argument shape must also be supplied
choice of distribution function for baseline hazard. Current options are: exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz
an object of class covmodel, see ?covmodel ?ExponentialCovFct or ?SpikedExponentialCovFct
mcmc control parameters, see ?mcmcpars
an object of class Priors, see ?mcmcPriors
when data is a data.frame, this can be a SpatialPolygonsDataFrame, or a SpatialPointsDataFrame, used to model spatial variation at the small region level. The regions are the polygons, or they represent the (possibly weighted) centroids of the polygons.
named list entry shpid character string giving name of variable in shape to be matched to variable dataid in data. dataid is the second entry of the named list.
additional control parameters, see ?inference.control
optional bounding box over which to construct computational grid, supplied as an object on which the function 'bbox' returns the bounding box
Benjamin M. Taylor and Barry S. Rowlingson (2017). spatsurv: An R Package for Bayesian Inference with Spatial Survival Models. Journal of Statistical Software, 77(4), 1-32, doi:10.18637/jss.v077.i04.
tpowHaz, exponentialHaz, gompertzHaz, makehamHaz, weibullHaz,
covmodel, linkExponentialCovFct, SpikedExponentialCovFct,
mcmcpars, mcmcPriors, inference.control