glsm.krige: Prediction for a Generalised Linear Spatial Model
In geoRglm: A Package for Generalised Linear Spatial Models

Description Usage Arguments Details Value Author(s) References See Also Examples

This function makes prediction for a generalised linear spatial model, using an output object from glsm.mcmc

1 2	glsm.krige(mcmc.output, locations, borders, trend.l, micro.scale=NULL, dist.epsilon= 1e-10, output)

`mcmc.output`	an output file from the function `glsm.mcmc`.
`locations`	an N x 2 matrix or data frame, or a list with the 2-D coordinates of the N prediction locations.
`borders`	optional. If a two column matrix defining a polygon is provided the prediction is performed only at locations inside this polygon.
`trend.l`	specifies the trend (covariate) values at prediction locations. It must be of the same type as for `trend`.
`micro.scale`	micro-scale variance. If specified, the nugget is divided into 2 terms: micro-scale variance and measurement error. This has effect on prediction, since the the target for prediction is inverse link function of the “signal” part of S (without the measurement error part of the nugget). The default is `micro.scale = nugget`.
`dist.epsilon`	a numeric value. Locations which are separated by a distance less than this value are considered co-located.
`output`	parameters for controlling the output. It can take an output from `output.glm.control` or a list with elements as for the arguments in `output.glm.control`. See documentation for `output.glm.control`.

This function makes prediction for fixed parameters using an output object from glsm.mcmc containing the model specification and simulations from the posterior values of S.

The prediction consist of performing trans-Gaussian kriging on each of the simulated g^{-1}(S)-“datasets” from the conditional distribution. Afterwards the predictor is obtained by taking the mean of prediction means, and the prediction variance is obtained by taking the mean of the prediction variances plus the variance of the prediction means. The trans-Gaussian kriging is done by calling an internal function which is an extension of krige.conv allowing for more than one “data set”, and using a second order Taylor approximation of the inverse link function g^{-1}.

A list with the following components:

`predict`	a vector with predicted values.
`krige.var`	a vector with predicted variances.
`mcmc.error`	estimated Monte Carlo errors on the predicted values.
`simulations`	an ni x n.sim matrix where ni is the number of prediction locations and n.sim is the number of MCMC simulations. Each column corresponds to a conditional simulation of the predictive distribution g^{-1}(S^{})*. Only returned if `sim.predict = TRUE`.
`message`	messages about the type of prediction performed.
`call`	the function call.

Ole F. Christensen OleF.Christensen@agrsci.dk,
Paulo J. Ribeiro Jr. Paulo.Ribeiro@est.ufpr.br.

Further information about geoRglm can be found at:
http://gbi.agrsci.dk/~ofch/geoRglm.

glsm.mcmc for MCMC simulation in a generalised linear spatial model.

if(!exists(".Random.seed", envir=.GlobalEnv, inherits = FALSE)) set.seed(1234)
data(b50)
mcmc.5 <- mcmc.control(S.scale = 0.6, thin=1)
model.5 <- list(cov.pars=c(0.6, 0.1), beta=1, family="binomial")
outmcmc.5 <- glsm.mcmc(b50, model= model.5, mcmc.input = mcmc.5)
test2 <- glsm.krige(outmcmc.5, locations=matrix(c(0.15,0.15,0.005,0.05),2,2))
image(test2)
test3 <- glsm.krige(outmcmc.5, locations=matrix(c(0.15,0.15,0.005,0.05),2,2),
                     output=output.glm.control(sim.predict=TRUE, quantile=FALSE))