Description Usage Arguments Details Value Author(s) See Also Examples
Simple plotting function for both discrete and continuous prediction from the object of class "Pred.SDALGCP".
1 2 3 4 5 6 7 8 9 10 |
x |
an object of class "Pred.SDALGCP" obtained as result of a call to |
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. |
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.
The function does not return any value.
Olatunji O. Johnson o.johnson@lancaster.ac.uk
Emanuele Giorgi e.giorgi@lancaster.ac.uk
Peter J. Diggle p.diggle@lancaster.ac.uk
SDALGCPPred, plot_continuous, plot_discrete, plot_SDALGCPexceedance, SDALGCPexceedance
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ### 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)
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