A function to control inferential settings. This function is used to set parameters for more advanced use of spatsurv.
inference.control(
gridded = FALSE,
cellwidth = NULL,
ext = 2,
imputation = NULL,
optimcontrol = NULL,
hessian = FALSE,
plotcal = FALSE,
timeonlyMCMC = FALSE,
nugget = FALSE,
savenugget = FALSE,
split = 0.5,
logUsigma_priormean = 0,
logUsigma_priorsd = 0.5,
nis = NULL,
olinfo = NULL
)returns parameters to be used in the function survspat
logical. Whether to perform compuation on a grid. Default is FALSE.
the width of computational cells to use
integer the number of times to extend the computational grid by in order to perform compuitation. The default is 2.
for polygonal data, an optional model for inference at the sub-polygonal level, see function imputationModel
a list of optional arguments to be passed to optim for non-spatial models
whether to return a numerical hessian. Set this to TRUE for non-spatial models. equal to the number of parameters of the baseline hazard
logical, whether to produce plots of the MCMC calibration process, this is a technical option and should onyl be set to TRUE if poor mixing is evident (the printed h is low), then it is also useful to use a graphics device with multiple plotting windows.
logical, whether to only time the MCMC part of the algorithm, or whether to include in the reported running time the time taken to calibrate the method (default)
whether to include a nugget effect in the estimation. Note that only the mean and variance of the nugget effect is returned.
whether to save the MCMC chain for the nugget effect
how to split the spatial and nugget proposal variance as a the proportion of variance assigned to the spatial effect apriori. Default is 0.5
prior mean for log standard deviation of nugget effect
prior sd for log standard deviation of nugget effect
list of cell counts, each element being a matrix, with attributes "x" and "y" giving grid centroids in x and y directions. Used to impute locations of aggregated data:.
to be supplied with nis, if continuous inference from aggregated data is required
survspat