SDADiscretePred: SDADiscretePred function

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

Description

This function performs spatial discrete prediction for a fixed the model parameters at the Monte Carlo maximum likelihood estimates of a SDALGCP model.

Usage

SDADiscretePred(
  para_est,
  control.mcmc = NULL,
  divisor = 1,
  plot.correlogram = FALSE,
  messages = TRUE
)

Arguments

para_est	an object of class "SDALGCP" obtained as a result of a call to SDALGCPMCML.
control.mcmc	output from controlmcmcSDA, if not provided, it uses the values used for the parameter estimation
divisor	optional, if the coordinate of the shapefile is rescaled, default is 1
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.

Details

The function returns the region-specific incidence and the covariate adjusted relative risk exp(S(A))

Value

It returns the following list

S.draw: the samples of the linear predictor d(x)'β + S(A)

incidence: the region-specific incidence

SEincidence: Standard error of the region-specific incidence

CovRR: the prediction of the covariate adjusted relative risk

SECovRR: the standard error of the covariate adjusted relative risk

Author(s)

Olatunji O. Johnson [e-mail]

Emanuele Giorgi [e-mail]

Peter J. Diggle [e-mail]

olatunjijohnson/SDALGCP documentation built on March 20, 2021, 4:24 a.m.