FastKRR (version 0.1.2)

predict.krr: Predict responses for new data using fitted KRR model

Description

Generates predictions from a fitted Kernel Ridge Regression (KRR) model for new data.

Usage

# S3 method for krr
predict(object, newdata, ...)

Value

A numeric vector of predicted values corresponding to newdata or fitted values.

Arguments

object

A S3 object of class krr created by fastkrr.

newdata

New design matrix or data frame containing new observations for which predictions are to be made. If newdata is missing, the function returns fitted values.

...

Additional arguments (currently ignored).

See Also

fastkrr, make_kernel

Examples

# Data setting
n = 30
d = 1
X = matrix(runif(n*d, 0, 1), nrow = n, ncol = d)
y = as.vector(sin(2*pi*rowMeans(X)^3) + rnorm(n, 0, 0.1))
lambda = 1e-4
rho = 1

# Fitting model: pivoted
model = fastkrr(X, y, kernel = "gaussian", rho = rho, lambda = lambda, opt = "pivoted")

# Predict
new_n = 50
new_x = matrix(runif(new_n*d, 0, 1), nrow = new_n, ncol = d)
new_y = as.vector(sin(2*pi*rowMeans(new_x)^3) + rnorm(new_n, 0, 0.1))

pred = predict(model, new_x)
crossprod(pred - new_y) / new_n

predict(model) == attributes(model)$fitted.values