Conformal predicions using locally weighted conformal inference with a split-conformal algorithm

confpred(object, data, newdata = data, alpha = 0.05, mad, ...)

Arguments

object

Model object (lm, glm or similar with predict method) or formula (lm)

data

data.frame

newdata

New data.frame to make predictions for

alpha

Level of prediction interval

mad

Conditional model (formula) for the MAD (locally-weighted CP)

...

Additional arguments to lower level functions

Value

data.frame with fitted (fit), lower (lwr) and upper (upr) predictions bands.

Examples

set.seed(123) n <- 200 x <- seq(0,6,length.out=n) delta <- 3 ss <- exp(-1+1.5*cos((x-delta))) ee <- rnorm(n,sd=ss) y <- (x-delta)+3*cos(x+4.5-delta)+ee d <- data.frame(y=y,x=x) newd <- data.frame(x=seq(0,6,length.out=50)) cc <- confpred(lm(y~splines::ns(x,knots=c(1,3,5)),data=d), data=d, newdata=newd) if (interactive()) { plot(y~x,pch=16,col=lava::Col("black"),ylim=c(-10,10),xlab="X",ylab="Y") with(cc, lava::confband(newd$x,lwr,upr,fit, lwd=3,polygon=TRUE,col=Col("blue"),border=FALSE)) }