cv.KERE {KERE} | R Documentation |
Cross-validation for KERE
Description
Does k-fold cross-validation for KERE
, produces a plot, and returns a value for lambda
.
Usage
## S3 method for class 'KERE'
cv(x, y, kern, lambda = NULL, nfolds = 5, foldid, omega = 0.5, ...)
Arguments
x |
matrix of predictors, of dimension |
y |
response variable. |
kern |
the built-in kernel classes in KERE.
The
Objects can be created by calling the rbfdot, polydot, tanhdot, vanilladot, anovadot, besseldot, laplacedot, splinedot functions etc. (see example.) |
lambda |
a user supplied |
nfolds |
number of folds - default is 5. Although |
foldid |
an optional vector of values between 1 and |
omega |
the parameter |
... |
other arguments that can be passed to |
Details
The function runs KERE
nfolds
+1 times; the
first to get the lambda
sequence, and then the remainder to
compute the fit with each of the folds omitted. The average error and standard deviation over the
folds are computed.
Value
an object of class cv.KERE
is returned, which is a
list with the ingredients of the cross-validation fit.
lambda |
the values of |
cvm |
the mean cross-validated error - a vector of length
|
cvsd |
estimate of standard error of |
cvupper |
upper curve = |
cvlo |
lower curve = |
name |
a character string "Expectile Loss" |
lambda.min |
the optimal value of |
cvm.min |
the minimum
cross validation error |
Author(s)
Yi Yang, Teng Zhang and Hui Zou
Maintainer: Yi Yang <yiyang@umn.edu>
References
Y. Yang, T. Zhang, and H. Zou. "Flexible Expectile Regression in Reproducing Kernel Hilbert Space." ArXiv e-prints: stat.ME/1508.05987, August 2015.
Examples
N <- 200
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))
cv.KERE(x=X, y=Y, kern, lambda = lambda, nfolds = 5, omega = 0.5)