as.list, covTP-method | R Documentation |
covTP
Object into a ListCoerce a covTP
object representing a Tensor-Product
covariance kernel on the d-dimensional Euclidean space
into a list containing d one-dimensional kernels.
## S4 method for signature 'covTP' as.list(x)
x |
A |
A list with length d
or d + 1
where d
is the
"dimension" slot x@d
of the object x
. The first d
elements of the list are one-dimensional correlation kernel
objects with class "covTP"
. When x
is a
covariance kernel (as opposed to a correlation kernel),
the list contains one more element which gives the variance.
When x
is not a correlation kernel the
(d + 1)
-th element of the returned list may be different in
future versions: it may be a constant covariance kernel.
covTP
and covTP-class
.
set.seed(123) d <- 6 myCov1 <- covTP(d = d, cov = "corr") coef(myCov1) <- as.vector(simulPar(myCov1, nsim = 1)) as.list(myCov1) ## more examples and check the value of a 'covMat' L <- list() myCov <- list() myCov[[1]] <- covTP(d = d, cov = "corr") coef(myCov[[1]]) <- as.vector(simulPar(myCov[[1]], nsim = 1)) L[[1]] <- as.list(myCov[[1]]) myCov[[2]] <- covTP(k1Fun1 = k1Fun1PowExp, d = d, cov = "corr") coef(myCov[[2]]) <- as.vector(simulPar(myCov[[2]], nsim = 1)) L[[2]] <- as.list(myCov[[2]]) myCov[[3]] <- covTP(k1Fun1 = k1Fun1PowExp, d = d, iso1 = 0L, cov = "corr") coef(myCov[[3]]) <- as.vector(simulPar(myCov[[3]], nsim = 1)) L[[3]] <- as.list(myCov[[3]]) n <- 10 X <- matrix(runif(n * d), nrow = n, dimnames = list(NULL, paste("x", 1:d, sep = ""))) for (iTest in 1:3) { C <- covMat(L[[iTest]][[1]], X[ , 1, drop = FALSE]) for (j in 2:d) { C <- C * covMat(L[[iTest]][[j]], X[ , j, drop = FALSE]) } CTest <- covMat(myCov[[iTest]], X) print(max(abs(abs(C - CTest)))) }