SDALGCPPred_ST: Spatial prediction using plug-in of MCML estimates

In SDALGCP: Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data

Description

This function performs spatial continuous and discrete prediction, fixing the model parameters at the Monte Carlo maximum likelihood estimates of a SDALGCP model.

Usage

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
)

Arguments

para_est	an object of class "SDALGCPST" obtained as a result of a call to SDALGCPMCML_ST.
cellsize	the size of the computational grid.
continuous	logical; to choose which prediction to do perform, discrete or continuous, the default is continuous.
control.mcmc	output from controlmcmcSDA, if not provided, it uses the values used for the parameter estimation.
pred.loc	optional, the dataframe of the predictive grid.
divisor	optional, the value to use to convert the dimension of the polygon, default is 1 which implies no conversion.
plot.correlogram	logical; if plot.correlogram = TRUE the autocorrelation plot of the conditional simulations is displayed.
messages	logical; if messages=TRUE then status messages are printed on the screen (or output device) while the function is running. Default is messages=TRUE.
parallel	to parallelize some part of the function.
n.window	the number of partitions to use for prediction. This is basically stratifying the predictive grid into fewer pieces

Details

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.

Value

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

Author(s)

Olatunji O. Johnson [e-mail]

Emanuele Giorgi [e-mail]

Peter J. Diggle [e-mail]

References

Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. CRC press.

See Also

plot.Pred.SDALGCPST, SDAContinuousPred, SDADiscretePred, plot_continuous, plot_discrete

Examples

# check vignette for examples

SDALGCP documentation built on March 17, 2021, 1:07 a.m.