predict.krige: Predictions by Kriging

In krige: Geospatial Kriging with Metropolis Sampling

Predictions by Kriging

Description

This function uses the results of a model estimated by metropolis.krige to make kriging-based predictions.

Usage

## S3 method for class 'krige'
predict(object, newdata, credible = FALSE, new.X, new.east, new.north, ...)

Arguments

object	An krige object estimated by metropolis.krige.
newdata	An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are produced. Alternatively, the new data can be specified using new.X, new.east, and new.north.
credible	If a credible interval on predictions is desired, a user may specify a proportion between 0 and 1 to indicate the interval probability. For example, a value of 0.9 would create a 90% credible interval. If NULL, then no credible interval will be produced.
new.X	The matrix of independent variables for observations to be predicted.
new.east	Vector of eastings for observations to be predicted.
new.north	Vector of northings for observations to be predicted.
...	Additional arguments passed to predict methods. Not supported for krige objects.

Details

Analysts should use this function if they want to make kriged predictions for observations at new locations or predict fitted values at original locations. To do this, researchers first must estimate a model using the metropolis.krige function.

After estimating the model, a krige object can provide information from the model fitted with metropolis.krige, including the original imput data, coordinates, and the model results themselves. The prediction will also use the same powered.exp. new.data can be specified for predicting the observations at new location. Otherwise, the fitted values for the original data and locations will be produced.

By default, the function uses median values of parameters to make a single point prediction for every kriged data point. However, if the uses specifies a probability with the credible option, then the function will determine the predictions for all iterations of the MCMC sample. The point estimates will then be a median of these predictions, and a credible interval will be returned based on percentiles. Note that estimating a credible interval is substantially more intensive computationally, but has the benefit of reporting uncertainty in predictions.

Value

An object of class matrix with one prediction per row. By default the matrix has one column, as only point predictions are returned. If the credible option is specified, there are three columns respectively indicating a point estimate (median prediction from MCMC), lower bound of the credible interval, and upper bound of the credible interval.

References

Jeff Gill. 2020. Measuring Constituency Ideology Using Bayesian Universal Kriging. State Politics & Policy Quarterly. doi:10.1177/1532440020930197

Examples

## Not run: 
# Summarize Data
summary(ContrivedData)

# Initial OLS model
contrived.ols<-lm(y~x.1+x.2,data=ContrivedData)
# summary(contrived.ols)

# Set seed
set.seed(1241060320)

M <- 100
#M<-10000

contrived.run <- metropolis.krige(y ~ x.1 + x.2, coords = c("s.1","s.2"), 
   data = ContrivedData, n.iter = M, range.tol = 0.05)
   
# Predict fitted values
predict(contrived.run)

# Predict new data
euler<-c(0.2,0.7)
archimedes<-c(0.3,0.1)
pythagoras<-c(0.1,0.4)
mathematicians<-rbind(euler,archimedes,pythagoras)
basel<-c(0.1,0.8)
sicily<-c(0.4,0.1)
samos<-c(0.1,0.4)
new.locations<-rbind(basel,sicily,samos)
newDf <- as.data.frame(cbind(mathematicians, new.locations))
colnames(newDf) <- c("x.1", "x.2", "s.1", "s.2")

# Make predictions from median parameter values:
(median.pred <- predict(contrived.run, newdata = newDf))

# Make predictions with 90\
(cred.pred <- predict(contrived.run, newdata = newDf, credible=0.9))

## End(Not run)

krige documentation built on May 1, 2022, 5:06 p.m.