| ploglik_xycholv {gear} | R Documentation |
Compute the log-likelihood of a model
Description
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.
Usage
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
)
Arguments
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 |
Value
A likelihood value, -2 times the likelihood value, or the estimated partial sill, depending on the user's argument choices.
References
Statistical Methods for Spatial Data Analysis. Oliver Schabenberger and Carol A. Gotway (Chapman & Hall/CRC Press) 2005. pp. 259-263
Examples
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)