Description Usage Arguments Value References Examples
ll_xycholv
computes the log-likelihood of
multivariate normal data using components typically found
in a geolm
. For ploglik_xycholv
,
cholv
is the cholesky decomposition of the
covariance matrix for the observed data after dividing
the matrix by the (estimated) psill
. See the
examples below.
Depending on parameter choices, the
function can return the log-likelihood, the restricted
log-likelihood, -2 times the log-likelihood or restricted
log-likelihood, or the estimated partial sill for both a
maximum likelihood and restricted maximum likelihood
setting. This is intended to be an internal function, so
minimal error checking is done.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ploglik_xycholv(
x,
y,
cholv,
mu = NULL,
reml = FALSE,
minus2 = TRUE,
return_ll = TRUE
)
ll_xycholv(
x,
y,
cholv,
mu = NULL,
reml = FALSE,
minus2 = TRUE,
return_ll = TRUE
)
|
x |
The matrix of covariates. |
y |
The vector of observed responses. |
cholv |
The cholesky decomposition of the covariance
matrix of |
mu |
A single numeric value indicating the assumed mean of the underlying process. |
reml |
A logical value. Should the Restricted
Maximum Likelihood be returned. The default is
|
minus2 |
A logical value. Should -2 times the
log-likelihood be returned. The default is |
return_ll |
A logical value. Should the
log-liklihood be returned? Default is |
A likelihood value, -2 times the likelihood value, or the estimated partial sill, depending on the user's argument choices.
Statistical Methods for Spatial Data Analysis. Oliver Schabenberger and Carol A. Gotway (Chapman & Hall/CRC Press) 2005. pp. 259-263
1 2 3 4 5 6 7 8 9 10 | y = rnorm(10)
x = matrix(rep(1, length(y)))
coords = matrix(runif(length(y) * 2), ncol = 2)
d = as.matrix(dist(coords))
pv = exp(-d/3) + 0.1 * diag(length(y))
est_psill = ploglik_xycholv(x, y, chol(pv), return_ll = FALSE)
v = pv * est_psill
# same result
ploglik_xycholv(x, y, chol(pv), minus2 = FALSE)
ll_xycholv(x, y, chol(v), minus2 = FALSE)
|
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