ramps (version 0.5-2)

predict.ramps: Prediction Method for georamps Model Fits

Description

Obtains prediction of main effects plus spatial variability from a georamps model fit.

Usage

## S3 method for class 'ramps':
predict(object, newdata, \dots)

Arguments

object

object returned by georamps.

newdata

data frame containing covariate values for the main effect, unmeasured spatial coordinates, and (if applicable) spatial variance indices with which to predict.

...

some methods for this generic require additional arguments. None are used in this method.

Value

• 'predict.ramps' object, inheriting from class 'matrix', of samples from the posterior predictive distribution. Labels for the samples at each new coordinate are supplied in the returned column names and MCMC iteration numbers in the row names. A matrix containing the new coordinates is supplied in the coords attribute of the object.

Details

Prediction will be performed only at the coordinates in newdata that differ from those used in the initial georamps model fitting. In particular, overlapping coordinates will be excluded automatically in the prediction.

See Also

georamps plot.predict.ramps, window.predict.ramps,

Examples

## Prediction for georamps example results

ct <- map("state", "connecticut", plot = FALSE)
lon <- seq(min(ct$x, na.rm = TRUE), max(ct$x, na.rm = TRUE), length = 20)
lat <- seq(min(ct$y, na.rm = TRUE), max(ct$y, na.rm = TRUE), length = 15)
grid <- expand.grid(lon, lat)

newsites <- data.frame(lon = grid[,1], lat = grid[,2])
newsites$measurement <- 1

NURE.pred <- predict(NURE.fit, newsites)

par(mfrow=c(2,1))
plot(NURE.pred, func = function(x) exp(mean(x)),
     database = "state", regions = "connecticut",
     resolution = c(200, 150), theta = 0.1,
     main = "Posterior Mean",
     legend.args = list(text = "ppm", side = 3, line = 1))
plot(NURE.pred, func = function(x) exp(sd(x)),
     database = "state", regions = "connecticut",
     resolution = c(200, 150), theta = 0.1,
     main = "Posterior Standard Deviation",
     legend.args = list(text = "ppm", side = 3, line = 1))