Awesome
<!-- README.md is generated from README.Rmd. Please edit that file -->R Package for Spatial Conformal Prediction
<!-- badges: start --> <!-- [](https://travis-ci.com/mhuiying/scp) --> <!-- [](https://ci.appveyor.com/project/mhuiying/scp) --> <!-- [](https://codecov.io/gh/mhuiying/scp?branch=master) --> <!-- badges: end -->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]