estimateBetaFields {SpatioTemporal} | R Documentation |
Estimates the latent-beta fields for a STdata
/STmodel
object
by regressing the observations for each site on the temporal trends.
estimateBetaFields(STdata = NULL, subset = NULL)
STdata |
A |
subset |
A subset of locations for which to estimate the beta-fields. A
warning is given for each name not found in |
A list with two matrices; the estimated beta-coefficients and standard deviations of the estimates.
Johan Lindstrom
Other data matrix: SVDmiss
,
SVDsmooth
, createDataMatrix
,
mesa.data.raw
Other STdata functions: c.STmodel
,
createDataMatrix
,
createSTdata
, createSTmodel
,
detrendSTdata
,
removeSTcovarMean
,
updateTrend.STdata
Other STmodel functions: createCV
,
createDataMatrix
,
createSTmodel
,
dropObservations
,
loglikeSTdim
, loglikeST
,
predictNaive
, processLUR
,
processLocation
, updateCovf
,
updateTrend.STdata
require(plotrix) ##load data data(mesa.model) ##Regression based estimate of the beta-fields beta <- estimateBetaFields(mesa.model) ##check regression coefficients summary(beta$beta) ##or plot as a function of distance to coast, ##with uncertainties par(mfrow=c(2,2)) for(i in 1:3){ plotCI(mesa.model$LUR[[1]][,"log10.m.to.a1"], beta$beta[,i], uiw=1.96*beta$beta.sd[,i], ylab=colnames(beta$beta)[i]) } ##or compare to the fields from predict.STmodel data(pred.mesa.model) ##Study the results ##Start by comparing beta fields par(mfcol=c(1,1), mar=c(4.5,4.5,2,.5), pty="s") plotCI(x=beta$beta[,1], y=pred.mesa.model$beta$EX[,1], uiw=1.96*sqrt(pred.mesa.model$beta$VX[,1]), main="Temporal Intercept", xlab="Empirical estimate", ylab="Spatio-Temporal Model") plotCI(x=beta$beta[,1], y=pred.mesa.model$beta$EX[,1], uiw=1.96*beta$beta.sd[,1], add=TRUE, err="x") abline(0,1,col="grey") ##or just the regression part of the beta fields points(x=beta$beta[,1], y=pred.mesa.model$beta$mu[,1], col=2, pch=19)