SpatioTemporal (version 1.1.7)

predictNaive: Naive Temporal Predictions

Description

Computes naive predictions that are based on a few sites. These predictions can then be used, e.g. in summary.predCVSTmodel, to evaluate how much better the spatial-temporal model performs compared to simple (temporal) predictions. The function requires one of location and type to be specified, if both are given location will be used over type. If type is given locations such that as.character(STmodel$locations$type) type will be used.

Usage

predictNaive(STmodel, locations = NULL, type = NULL)

Arguments

STmodel

STmodel object for which to compute simple predictions.

locations

Locations on which to base the naive predictions.

type

The type of sites to base the predictions on, uses the (optional) field STmodel$locations$type.

Value

A list with items:

pred

A (number of observations) - by - (6) data.frame containing the four naive predictions described under details, along with dates and IDs.

locations

The locations used for the naive predictions.

Details

Given a set of locations the function computes 4 sets of naive prediction for the observations in STmodel:

smooth.fixed

The smooth trend in STmodel$trend is fit to all observations at the sites in locations using a linear regression. The resulting smooth is used as a naive prediction for all locations.

avg.fixed

The temporal average over sites in locations is used as a naive prediction.

smooth.closest.fixed

This fits the smooth trend in STmodel$trend to each site in locations; using the smooth at the closest fixed site as a naive prediction.

closest.fixed

This uses the observations at the closest site in locations to predict observations at all other sites.

See Also

Other cross-validation functions: computeLTA, createCV, dropObservations, estimateCV, estimateCV.STmodel, predictCV, predictCV.STmodel

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

Examples

# NOT RUN {
##load data
data(mesa.model)

##naive predictions based on either AQS,
pred.aqs <- predictNaive(mesa.model, type="AQS")
##...or only one sites,
pred.1site <- predictNaive(mesa.model, locations="60372005")

##plot the predictions - The two cases that are constant in space
par(mfcol=c(2,1), mar=c(4.5,4.5,1,.5))

##observations as a function of date
plot(mesa.model, "loc.obs", type=as.factor(mesa.model$locations$ID),
     legend.loc=NULL, pch=19, cex=.25)
##Add the predictions based on the smooth fitted to all sites
with(pred.aqs$pred, lines(date, smooth.fixed, col=1, lwd=2) )
with(pred.1site$pred, lines(date, smooth.fixed, col=2, lwd=2) )

##plot the predictions - One of the cases that vary in space, i.e. the smooth
##fit to the closest site.
##first extract as a data matrix
D <- with(pred.aqs$pred, createDataMatrix(obs=smooth.closest.fixed,
                                          date=date, ID=ID) )

##observations as a function of date
##(only five sites for clarity)
mesa.model <- dropObservations(mesa.model, !(mesa.model$obs$idx %in% c(1,2,3,23,24)))
plot(mesa.model, "loc.obs", type=as.factor(mesa.model$locations$ID),
     legend.loc=NULL, pch=19, cex=.25)
##Add the predictions based on the smooth
##fitted to the closest site
for(i in 1:5){
  lines(as.Date(rownames(D)), D[,mesa.model$locations$ID[i]], col=i, lwd=2)
}
# }