Description Usage Arguments Value Warning Author(s) See Also Examples
Computes the log-likelihood for the spatio-temporal model. loglikeST
uses an optimised version of the log-likelihood, while loglikeSTnaive
uses the naive (slow) version and is included mainly for testing and speed
checks.
1 2 3 |
x |
Point at which to compute the log-likelihood, should be only
log-covariance parameters if |
STmodel |
|
type |
A single character indicating the type of log-likelihood to compute. Valid options are "f", "p", and "r", for full, profile or restricted maximum likelihood (REML). |
x.fixed |
Parameters to keep fixed, |
Returns the log-likelihood of the spatio temporal model.
loglikeSTnaive
may take long to run. However for
some problems with many locations and short time series
loglikeSTnaive
could be faster than loglikeST
.
Johan Lindstrom
Other STmodel functions: createCV
,
createDataMatrix
,
createSTmodel
,
dropObservations
,
estimateBetaFields
,
loglikeSTdim
, predictNaive
,
processLUR
, processLocation
,
updateCovf
,
updateTrend.STdata
Other likelihood functions: loglikeSTGrad
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ##load the data
data(mesa.model)
##Compute dimensions for the data structure
dim <- loglikeSTdim(mesa.model)
##Find out in which order parameters should be given
loglikeST(NULL, mesa.model)
##Let's create random vectors of values
x <- runif( dim$nparam.cov )
x.all <- runif( dim$nparam )
##Evaluate the log-likelihood for these values
loglikeST(x.all, mesa.model, "f")
loglikeST(x, mesa.model, "p")
|
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