scpm (version 2.0.0)

scpm-package: 'An R Package for Spatial Smoothing'

Description

Group of functions for spatial smoothing using cubic splines and variogram maximum likelihood estimation. Also allow the inclusion of linear parametric terms and change-points for segmented smoothing splines models.

Examples

# NOT RUN {
data(landim1, package = "scpm")
if(FALSE){
library(scpm)
##create tthe dataset
d <- as.sss(landim1, coords = NULL, coords.col = 1:2, data.col = 3:4)
##fitting spatial linear model with response A and covariate B
##Gneiting covariance function in the errors
m0 <- scp(A ~ linear(~ B), data = d, model = "RMgneiting")
##adding a bivariate cubic spline based on the coordinates
m1 <- scp(A ~ linear(~ B) + s2D(penalty = "cs"), data = d, model = "RMgneiting")
##plotting observed and estimated field from each model
par(mfrow=c(2,2))
plot(m0, what = "obs", type = "persp", main = "Model null - y")
plot(m0, what = "fit", type = "persp", main = "Model null - fit")
plot(m1, what = "obs", type = "persp", main = "Model alternative - y")
plot(m1, what = "fit", type = "persp", main = "Model alternative - fit")
##plotting the estimated semivariogram from each model
par(mfrow=c(1,2))
Variogram(m0,main="Semivariogram - model null", ylim = c(0,0.7))
Variogram(m1,main="Semivariogram - model alternative", ylim = c(0,0.7))
##summary of the estimated coefficients
summary(m0)
summary(m1)
##some information criteria
AIC(m0)
AIC(m1)
AICm(m0)
AICm(m1)
AICc(m0)
AICc(m1)
BIC(m0)
BIC(m1)
}
# }