mhuiying / scp

R Package for Spatial Conformal Prediction

R Package for Spatial Conformal Prediction

The goal of “scp” is to provide valid model-free spatial prediction intervals.

Installation

The current development version can be installed from source using devtools.

devtools::install_github("mhuiying/scp", build_vignettes = TRUE)

Example

library(scp)

# an example sample data
data('sample_data')
s  = sample_data$s
Y  = sample_data$Y

# locations to predict
s0  = c(0.5,0.5)
s0s = rbind(c(0.4, 0.4), c(0.5,0.5), c(0.6, 0.6))

# default prediction interval
scp(s0=s0,s=s,Y=Y)
scp(s0=s0s,s=s,Y=Y)

# user define eta=0.1, where LSCP is considered
scp(s0=s0,s=s,Y=Y,eta=0.1)

# user define non-conformity measure
scp(s0=s0,s=s,Y=Y,dfun="std_residual2")

# user define prediction function
fun = function(s0,s,Y) return(mean(Y))
scp(s0=s0,s=s,Y=Y,pred_fun=fun)

Want more example, please check our vignettes.

browseVignettes('scp')

References

Mao, Huiying, Ryan Martin, and Brian Reich. Valid model-free spatial prediction, 2020. [arxiv]