SDAContinuousPred: SDAContinuousPred function

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

Description

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

Usage

SDAContinuousPred(
  para_est,
  cellsize,
  control.mcmc = NULL,
  pred.loc = NULL,
  divisor = 1,
  plot.correlogram = F,
  messages = TRUE,
  parallel = FALSE
)

Arguments

para_est	an object of class "SDALGCP" obtained as a result of a call to SDALGCPMCML.
cellsize	the size of the computational grid
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.

Details

The function returns the prediction of the relative risk exp(S(x))

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]

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