engression (version 0.1.5)

predict.engression: Prediction Function for Engression Models

Description

This function computes predictions from a trained engression model. It allows for the generation of point estimates, quantiles, or samples from the estimated distribution.

Usage

# S3 method for engression
predict(
  object,
  Xtest,
  type = c("mean", "sample", "quantile")[1],
  trim = 0.05,
  quantiles = 0.1 * (1:9),
  nsample = 200,
  drop = TRUE,
  ...
)

Value

A matrix or array of predictions.

Arguments

object

A trained engression model returned from engression, engressionBagged or engressionfit functions.

Xtest

A matrix or data frame representing the predictors in the test set.

type

The type of prediction to make. "mean" for point estimates, "sample" for samples from the estimated distribution, or "quantile" for quantiles of the estimated distribution (default: "mean").

trim

The proportion of extreme values to trim when calculating the mean (default: 0.05).

quantiles

The quantiles to estimate if type is "quantile" (default: 0.1*(1:9)).

nsample

The number of samples to draw if type is "sample" (default: 200).

drop

A boolean indicating whether to drop dimensions of length 1 from the output (default: TRUE).

...

additional arguments (currently ignored)

Examples

# \donttest{
  n = 1000
  p = 5

  X = matrix(rnorm(n*p),ncol=p)
  Y = (X[,1]+rnorm(n)*0.1)^2 + (X[,2]+rnorm(n)*0.1) + rnorm(n)*0.1
  Xtest = matrix(rnorm(n*p),ncol=p)
  Ytest = (Xtest[,1]+rnorm(n)*0.1)^2 + (Xtest[,2]+rnorm(n)*0.1) + rnorm(n)*0.1

  ## fit engression object
  engr = engression(X,Y)
  print(engr)

  ## prediction on test data
  Yhat = predict(engr,Xtest,type="mean")
  cat("\n correlation between predicted and realized values:  ", signif(cor(Yhat, Ytest),3))
  plot(Yhat, Ytest,xlab="prediction", ylab="observation")

  ## quantile prediction
  Yhatquant = predict(engr,Xtest,type="quantiles")
  ord = order(Yhat)
  matplot(Yhat[ord], Yhatquant[ord,], type="l", col=2,lty=1,xlab="prediction", ylab="observation")
  points(Yhat[ord],Ytest[ord],pch=20,cex=0.5)

  ## sampling from estimated model
  Ysample = predict(engr,Xtest,type="sample",nsample=1)
  
# }