SDALGCP (version 0.4.0)

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

Description

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

Usage

# S3 method for 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.

Value

The function does not return any value.

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.

See Also

SDALGCPPred, plot_continuous, plot_discrete, plot_SDALGCPexceedance, SDALGCPexceedance

Examples

# NOT RUN {
### 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
# }
# NOT RUN {
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)
# }