Synthetic multivariate data with spatial and non-spatial variance structures
The synthetic dataset describes a stationary and isotropic bivariate process. Please refer to the vignette Section 4.2 for specifics.
data(FBC07.dat)
A data frame of 250 rows and 4 columns. Columns 1 and 2 are coordinates and columns 3 and 4 are response variables.
Finley A.O., S. Banerjee, and B.P. Carlin (2007) spBayes: R package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models. Journal of Statistical Software.
## Not run:
data(FBC07.dat)
library(geoR)
max <- 40
bins <- 20
pts <- 1:150
vario.1 <- variog(coords=FBC07.dat[pts,1:2], data=FBC07.dat[pts,3],
uvec=(seq(0, max, length=bins)))
vario.2 <- variog(coords=FBC07.dat[pts,1:2], data=FBC07.dat[pts,4],
uvec=(seq(0,max, length=bins)))
vario.fit.1 <-variofit(vario.1, ini.cov.pars=c(5.0, 1.0),
cov.model="exponential",
minimisation.function="nls",
weights="equal")
vario.fit.2 <-variofit(vario.2, ini.cov.pars=c(5.0, 10.0),
cov.model="exponential",
minimisation.function="nls",
weights="equal")
par(mfrow=c(1,2))
plot(vario.1$u, vario.1$v, axes=FALSE, type = "n",
ylim=c(0,15), xlab="Distance", ylab="Semivariance")
points(vario.1$u, vario.1$v, pch=19, cex=0.5)
axis(1, seq(0,max,10))
axis(2, seq(0,15,5))
abline(h=vario.fit.1$nugget)
abline(h=vario.fit.1$cov.pars[1]+vario.fit.1$nugget)
abline(v=3/(1/vario.fit.1$cov.pars[2]))
lines(vario.fit.1)
plot(vario.2$u, vario.2$v, axes=FALSE, type = "n",
ylim=c(0,15), xlab="Distance", ylab="")
points(vario.2$u, vario.2$v, pch=19, cex=0.5)
axis(1, seq(0,max,10))
abline(h=vario.fit.2$nugget)
abline(h=vario.fit.2$cov.pars[1]+vario.fit.2$nugget)
abline(v=3/(1/vario.fit.2$cov.pars[2]))
lines(vario.fit.2)
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