predict.KERE: make predictions from a "KERE" object.
In emeryyi/KERE: Expectile Regression in Reproducing Kernel Hilbert Space

Description Usage Arguments Details Value Author(s) References Examples

Similar to other predict methods, this functions predicts fitted values and class labels from a fitted KERE object.

1 2	## S3 method for class 'KERE' predict(object, kern, x, newx,...)

`object`	fitted `KERE` model object.
`kern`	the built-in kernel classes in KERE. Objects can be created by calling the rbfdot, polydot, tanhdot, vanilladot, anovadot, besseldot, laplacedot, splinedot functions etc. (see example.)
`x`	the original design matrix for training `KERE`.
`newx`	matrix of new values for `x` at which predictions are to be made. NOTE: `newx` must be a matrix with each row as an observation. `predict` function does not accept a vector or other formats of `newx`.
`...`	other parameters to `predict` function.

The fitted α_0 + K * α at newx is returned as a size nrow(newx)*length(lambda) matrix for various lambda values where the KERE model was fitted.

The fitted α_0 + K * α is returned as a size nrow(newx)*length(lambda) matrix. The row represents the index for observations of newx. The column represents the index for the lambda sequence.

Yi Yang, Teng Zhang and Hui Zou
Maintainer: Yi Yang <yi.yang6@mcgill.ca>

Y. Yang, T. Zhang, and H. Zou. (2017) "Flexible Expectile Regression in Reproducing Kernel Hilbert Space." Technometrics. Accepted.

# create data
N <- 100
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- 3*runif(N)
SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + X1*X3
sigma <- sqrt(var(Y)/SNR)
Y <- Y + X2*rnorm(N,0,sigma)
X <- cbind(X1,X2,X3)

# set gaussian kernel 
kern <- rbfdot(sigma=0.1)

# define lambda sequence
lambda <- exp(seq(log(0.5),log(0.01),len=10))

# run KERE
m1 <- KERE(x=X, y=Y, kern=kern, lambda = lambda, omega = 0.5) 

# create newx for prediction
N1 <- 5
X1 <- runif(N1)
X2 <- 2*runif(N1)
X3 <- 3*runif(N1)
newx <- cbind(X1,X2,X3)

# make prediction
p1 <- predict.KERE(m1, kern, X, newx)
p1