| covmat {spam} | R Documentation |
Evaluate a covariance function.
covmat(h, theta, ... , type="sph")
cov.exp(h, theta, ... , eps= getOption("spam.eps"))
cov.sph(h, theta, ... , eps= getOption("spam.eps"))
cov.nug(h, theta, ... , eps= getOption("spam.eps"))
cov.wend1(h, theta, ... , eps= getOption("spam.eps"))
cov.wend2(h, theta, ... , eps= getOption("spam.eps"))
cov.wu1(h, theta, ... , eps= getOption("spam.eps"))
cov.wu2(h, theta, ... , eps= getOption("spam.eps"))
cov.wu3(h, theta, ... , eps= getOption("spam.eps"))
cov.mat(h, theta, ... , eps= getOption("spam.eps"))
cor.sph(h, range, ... , eps= getOption("spam.eps"))
h |
object containing the lags. |
theta |
parameter of the covariance function, see ‘Details’. |
range |
parameter defining the compact support. |
type |
covariance function specification. |
... |
arguments passed from other methods. |
eps |
tolerance level, see ‘Details’. |
covmat is a wrapper that calls the other functions
according to the argument type. The nomenclature is similar to
premat.
The parametrization is (range, partial-sill, [smoothness = 1], [nugget = 0]), where
only the range needs to be specified.
In case of negative parameter values, a warning is issued and the
absolute value is retained.
Although more cryptic, having all arguments as a single vector
simplifies optimization with optim.
The parameters are and locations are up to precision
epsilon. That means that all distances smaller than eps
are considered zero, nugget smaller than eps is ignored, etc.
cor.sph(,range) is a fast version of cov.sph(,c(range,1,0)).
Currently, the functions distinguish between a sparse spam
object h and any other numeric type. In the future, this might
change and appropriate methods will be implemented.
Covariance function evaluated on h.
Reinhard Furrer
Any classical book about geostatistics.
set.seed(123)
n <- 200
locs <- cbind(runif(n),runif(n))
h <- nearest.dist(locs, delta=sqrt(2), upper = NULL)
Sigma <- cov.sph(h, c(.3, 1, .1))
iidsample <- rnorm(n)
cholS <- chol.spam(as.spam(Sigma))
iorder <- iord <- ordering(cholS, inv = TRUE)
sample <- (iidsample %*% as.spam(cholS))[iorder]
plot(locs, col = fields::tim.colors(n = 256)[cut(sample, n)], pch = 20)
## Not run:
h <- seq(0, to=1, length.out=100)
plot( h, cov.exp(h, c(1/3,1)), type='l', ylim=c(0,1))
type <- c("sph","wendland1","wendland2","wu1","wu2","wu3")
for (i in 1:6)
lines( h, covmat(h, 1, type=type[i]), col=i+1)
legend('topright',legend=type, col=2:7, lty=1)
## End(Not run)