SpatioTemporal (version 1.1.7)

loglikeST: Compute the Log-likelihood for the Spatio-Temporal Model

Description

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.

Usage

loglikeST(x = NULL, STmodel, type = "p", x.fixed = NULL)
  loglikeSTnaive(x = NULL, STmodel, type = "p",
    x.fixed = NULL)

Arguments

x

Point at which to compute the log-likelihood, should be only log-covariance parameters if type=c("p","r") and regression parameters followed by log-covariance parameters if type="f". If x=NULL the function acts as an alias for loglikeSTnames returning the expected names of the input parameters.

STmodel

STmodel object with the model for which to compute the log-likelihood.

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, NA values in this vector is replaced by values from x and the result is used as x, ie. x.fixed[ is.na(x.fixed) ] <- x x <- x.fixed .

Value

Returns the log-likelihood of the spatio temporal model.

Warning

loglikeSTnaive may take long to run. However for some problems with many locations and short time series loglikeSTnaive could be faster than loglikeST.

See Also

Other likelihood functions: loglikeSTGrad, loglikeSTHessian, loglikeSTnaiveGrad, loglikeSTnaiveHessian

Other STmodel functions: createCV, createDataMatrix, createSTmodel, dropObservations, estimateBetaFields, loglikeSTdim, predictNaive, processLocation, processLUR, processST, updateCovf, updateSTdataTrend, updateTrend, updateTrend.STdata, updateTrend.STmodel

Examples

# NOT RUN {
##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")

# }