plot.Pred.SDALGCP: plot.Pred.SDALGCP function

In 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.SDALGCP".

Usage

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

Arguments

x	an object of class "Pred.SDALGCP" obtained as result of a call to SDALGCPPred.
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
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, plot_continuous, plot_discrete, plot_SDALGCPexceedance, SDALGCPexceedance

Examples

### Prepare the input of the model
data(PBCshp)
data <- as.data.frame(PBCshp@data)  #get the data
### Write the formula of the model
FORM <- X ~ propmale + Income + Employment + Education + Barriers + Crime +
Environment +  offset(log(pop))
### set the discretised phi
phi <- seq(500, 1700, length.out = 20)
#### get the initial parameter
model <- glm(formula=FORM, family="poisson", data=data)
beta.start <-coef(model)
sigma2.start <- mean(model$residuals^2)
phi.start <- median(phi)
par0 <- c(beta.start, sigma2.start, phi.start)
# setup the control arguments for the MCMC
n <- 545
h <- 1.65/(n^(1/6))
control.mcmc <- controlmcmcSDA(n.sim = 10000, burnin = 2000,
                 thin= 8, h=h, c1.h = 0.01, c2.h = 1e-04)
###Run the model

my_est <- SDALGCPMCML(formula=FORM, data=data, my_shp=PBCshp, delta=100, phi=phi, method=1,
                     weighted=FALSE,  plot=TRUE, par0=NULL, control.mcmc=control.mcmc)
Con_pred <- SDALGCPPred(para_est=my_est,  cellsize=300, continuous=TRUE)
#to plot the spatially continuous relative risk
plot(Con_pred, type="relrisk")
#to plot the incidence
plot(Con_pred, type="incidence", continuous=FALSE)
#to plot the exceedance probability of the relative risk
plot(Con_pred, type="relrisk", thresholds= 2)
#to plot the exceedance probability of the incidence
plot(Con_pred, type="incidence", continuous=FALSE, thresholds= 0.001)

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