cor_fs {mcgf} | R Documentation |
Calculate correlation for fully symmetric model
Description
Calculate correlation for fully symmetric model
Usage
cor_fs(nugget = 0, c, gamma = 1/2, a, alpha, beta = 0, h, u)
Arguments
nugget |
The nugget effect |
c |
Scale parameter of |
gamma |
Smooth parameter of |
a |
Scale parameter of |
alpha |
Smooth parameter of |
beta |
Interaction parameter, |
h |
Euclidean distance matrix or array. |
u |
Time lag, same dimension as |
Details
The fully symmetric correlation function with interaction parameter
has the form
where is the Euclidean distance, and
is 1
when
and 0 otherwise. Here
and
. By default
beta = 0
and it reduces to the separable
model.
Value
Correlations of the same dimension as h
and u
.
References
Gneiting, T. (2002). Nonseparable, Stationary Covariance Functions for Space–Time Data, Journal of the American Statistical Association, 97:458, 590-600.
See Also
Other correlation functions:
cor_cauchy()
,
cor_exp()
,
cor_lagr_askey()
,
cor_lagr_exp()
,
cor_lagr_tri()
,
cor_sep()
,
cor_stat()
,
cor_stat_rs()
Examples
h <- matrix(c(0, 5, 5, 0), nrow = 2)
u <- matrix(0, nrow = 2, ncol = 2)
cor_fs(c = 0.01, gamma = 0.5, a = 1, alpha = 0.5, beta = 0.5, h = h, u = u)
h <- array(c(0, 5, 5, 0), dim = c(2, 2, 3))
u <- array(rep(0:2, each = 4), dim = c(2, 2, 3))
cor_fs(c = 0.01, gamma = 0.5, a = 1, alpha = 0.5, beta = 0.5, h = h, u = u)