plot.Pred.SDALGCPST: plot.Pred.SDALGCPST function

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

Description

Simple plotting function for both discrete and continuous prediction from the object of class "Pred.SDALGCPST".

Usage

## S3 method for class 'Pred.SDALGCPST'
plot(
  x,
  type = "relrisk",
  continuous = NULL,
  thresholds = NULL,
  bound = NULL,
  overlay = FALSE,
  ...
)

Arguments

x	an object of class "Pred.SDALGCPST" obtained as result of a call to SDALGCPPred_ST.
type	Character string: what type of plot to produce. For discrete inference choices are "incidence" (=exp(mu+S)); "SEincidence" (standard error of incidence); "CovAdjRelRisk" (=exp(S)); or "SECovAdjRelRisk" (standard error of covariate adjusted relative risk); while for continuous inference, choices are "relrisk" (=exp(S)); "SErelrisk" (standard error of the relative risk).
continuous	logical; TRUE for spatially continuous relative risk and FALSE for region specific relative risk. default is TRUE
thresholds	optional; (only used if you want to plot the exceedance probability) either a vector of numbers or a vector of single value.
bound	optional; it gives the boundary of the region, only useful when the predictive location is supplied in SDALGCPPred_ST
overlay	optional; a logical operation to indicate either to add a base map.
...	further arguments passed to plot.

Details

This function plots the inference from SDALGCPPred function. It plots for region-specific inference; incidence and covariate adjusted relative risk while for spatially continuous inference it plots the relative risk. It can as well plot the exceedance probability for spatially discrete and continuous inference.

Value

The function does not return any value.

Author(s)

Olatunji O. Johnson [e-mail]

Emanuele Giorgi [e-mail]

Peter J. Diggle [e-mail]

See Also

SDALGCPPred_ST, plot_continuousST, plot_discreteST, plot_SDALGCPexceedanceST, SDALGCPexceedanceST

Examples

# check vignette for examples

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