This function performs spatial continuous and discrete prediction, fixing the model parameters at the Monte Carlo maximum likelihood estimates of a SDALGCP model.
SDALGCPPred_ST(
para_est,
cellsize,
continuous = TRUE,
control.mcmc = NULL,
pred.loc = NULL,
divisor = 1,
plot.correlogram = F,
messages = TRUE,
parallel = FALSE,
n.window = 1
)an object of class "SDALGCPST" obtained as a result of a call to SDALGCPMCML_ST.
the size of the computational grid.
logical; to choose which prediction to do perform, discrete or continuous, the default is continuous.
output from controlmcmcSDA, if not provided, it uses the values used for the parameter estimation.
optional, the dataframe of the predictive grid.
optional, the value to use to convert the dimension of the polygon, default is 1 which implies no conversion.
logical; if plot.correlogram = TRUE the autocorrelation plot of the conditional simulations is displayed.
logical; if messages=TRUE then status messages are printed on the screen (or output device) while the function is running. Default is messages=TRUE.
to parallelize some part of the function.
the number of partitions to use for prediction. This is basically stratifying the predictive grid into fewer pieces
pred.draw: the samples of the prediction
pred: the prediction of the relative risk
predSD: the standard error of the prediction
Pred.loc: The coordinates of the predictive locations
The function perform prediction of the spatially discrete incidence and covariate adjusted relative risk, and spatially continuous relative risk. The discrete inference uses the Metropolis-Adjusted Langevin Hasting sampling from Laplace.sampling. And the continuous inference is typically change of support inference.
Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. CRC press.
plot.Pred.SDALGCPST, SDAContinuousPred, SDADiscretePred, plot_continuous, plot_discrete
# NOT RUN {
# check vignette for examples
# }
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