ContrivedData {krige}

Contrived Example Data

Description

These data are a simulated point-referenced geospatial data that serve to provide a clean example of a kriging model. There are 500 observations with coordinates located on a unit square.

Format

The ContrivedData dataset has 500 observations and 5 variables.

y

The outcome variable. Its true population functional form is y_s=0+1 x_{1s}+2 x_{2s}+\omega_{s}+\epsilon_{s}. The true variance of \omega is \sigma^2=0.5 and of \epsilon is \tau^2=0.5. The decay term that shapes spatial correlation levels is \phi=2.5.

x.1

A predictor with a standard uniform distribution.

x.2

A predictor with a standard normal distribution.

s.1

Coordinate in eastings for each observation, distributed standard uniform.

s.2

Coordinate in northings for each observation, distributed standard uniform.

Examples

## Not run: 
# Summarize example data
summary(ContrivedData)

# Initial OLS model
contrived.ols<-lm(y~x.1+x.2,data=ContrivedData)
# summary(contrived.ols)

# Set seed
set.seed(1241060320)

#For simple illustration, we set to few iterations.
#In this case, a 10,000-iteration run converges to the true parameters.
#If you have considerable time and hardware, delete the # on the next line.
#10,000 iterations took 39 min. with 8 GB RAM & a 1.5 GHz Quad-Core processor.
M <- 100
#M<-10000

contrived.run <- metropolis.krige(y ~ x.1 + x.2, coords = c("s.1","s.2"), 
   data = ContrivedData, n.iter = M, n.burnin=20, range.tol = 0.05)
# Alternatively, use burnin() after estimation  
#contrived.run <- burnin(contrived.run, n.burnin=20)

# Summarize the results and examine results against true coefficients	
summary(contrived.run)
(TRUTH<-c(0.5,2.5,0.5,0,1,2))

## End(Not run)