Description Usage Arguments Details Value Author(s) References Examples
Does kfold crossvalidation for KERE
, produces a plot, and returns a value for lambda
.
1 2 
x 
matrix of predictors, of dimension N*p; each row is an observation vector. 
y 
response variable. 
kern 
the builtin 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 omega in the expectile regression model. The value must be in (0,1). Default is 0.5. 
... 
other arguments that can be passed to 
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.
an object of class cv.KERE
is returned, which is a
list with the ingredients of the crossvalidation fit.
lambda 
the values of 
cvm 
the mean crossvalidated 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 
Yi Yang, Teng Zhang and Hui Zou
Maintainer: Yi Yang <[email protected]>
Y. Yang, T. Zhang, and H. Zou. "Flexible Expectile Regression in Reproducing Kernel Hilbert Space." ArXiv eprints: stat.ME/1508.05987, August 2015.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  N < 200
X1 < runif(N)
X2 < 2*runif(N)
X3 < 3*runif(N)
SNR < 10 # signaltonoise 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)

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