Description Usage Arguments Value References See Also
A function to run a Bayesian analysis on censored spatial survial data assuming a proportional hazards model using an adaptive Metropolis-adjusted Langevin algorithm.
1 2 3 |
formula |
the model formula in a format compatible with the function flexsurvreg from the flexsurv package |
data |
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 |
dist |
choice of distribution function for baseline hazard. Current options are: exponentialHaz, weibullHaz, gompertzHaz, makehamHaz, tpowHaz |
cov.model |
an object of class covmodel, see ?covmodel ?ExponentialCovFct or ?SpikedExponentialCovFct |
mcmc.control |
mcmc control parameters, see ?mcmcpars |
priors |
an object of class Priors, see ?mcmcPriors |
shape |
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. |
ids |
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. |
control |
additional control parameters, see ?inference.control |
boundingbox |
optional bounding box over which to construct computational grid, supplied as an object on which the function 'bbox' returns the bounding box |
an object inheriting class 'mcmcspatsurv' for which there exist methods for printing, summarising and making inference from.
Benjamin M. Taylor. Auxiliary Variable Markov Chain Monte Carlo for Spatial Survival and Geostatistical Models. Benjamin M. Taylor. Submitted. http://arxiv.org/abs/1501.01665
tpowHaz, exponentialHaz, gompertzHaz, makehamHaz, weibullHaz,
covmodel, linkExponentialCovFct, SpikedExponentialCovFct
,
mcmcpars, mcmcPriors, inference.control
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