Description Usage Arguments Details Value References Examples
Does k-fold cross-validation for krome
, 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 built-in kernel classes in krome.
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 |
delta |
the parameter delta in the huber regression loss. The value must be positive. Default is 2. |
... |
other arguments that can be passed to |
The function runs krome
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.krome
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 |
Y. Yang, T. Zhang, and H. Zou. (2017) "Flexible Expectile Regression in Reproducing Kernel Hilbert Space." Technometrics. Accepted.
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 # 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.krome(x=X, y=Y, kern, lambda = lambda, nfolds = 5, delta = 2)
|
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