predict.KERE {KERE} | R Documentation |
Similar to other predict methods, this functions predicts fitted values and class labels from a fitted KERE
object.
## S3 method for class 'KERE' predict(object, kern, x, newx,...)
object |
fitted |
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 |
newx |
matrix of new values for |
... |
other parameters to |
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 <yiyang@umn.edu>
Y. Yang, T. Zhang, and H. Zou. "Flexible Expectile Regression in Reproducing Kernel Hilbert Space." ArXiv e-prints: stat.ME/1508.05987, August 2015.
# 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