predict.krome: make predictions from a "krome" object.
In emeryyi/rome: Robust Mean Estimation

Description Usage Arguments Details Value References Examples

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

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

`object`	fitted `krome` model object.
`kern`	the built-in kernel classes in krome. 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 `krome`.
`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 krome 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.

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 krome
m1 <- krome(x=X, y=Y, kern=kern, lambda = lambda, delta = 2) 

# 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.krome(m1, kern, X, newx)
p1