cv.nclreg_fit {mpath} | R Documentation |
Internal function of cross-validation for nclreg
Description
Internal function to conduct k-fold cross-validation for nclreg, produces a plot,
and returns cross-validated loss values for lambda
Usage
cv.nclreg_fit(x, y, weights, offset, lambda=NULL, balance=TRUE,
rfamily=c("clossR", "closs", "gloss", "qloss"), s=1.5,
nfolds=10, foldid, type = c("loss", "error"),
plot.it=TRUE, se=TRUE, n.cores=2, trace=FALSE,
parallel=FALSE, ...)
Arguments
x |
|
y |
response |
weights |
Observation weights; defaults to 1 per observation |
offset |
this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. Currently only one offset term can be included in the formula. |
lambda |
Optional user-supplied lambda sequence; default is
|
balance |
for |
rfamily |
response variable distribution and nonconvex loss function |
s |
nonconvex loss tuning parameter for robust regression and classification. |
nfolds |
number of folds >=3, default is 10 |
foldid |
an optional vector of values between 1 and |
type |
cross-validation criteria. For |
plot.it |
a logical value, to plot the estimated loss values if |
se |
a logical value, to plot with standard errors. |
n.cores |
The number of CPU cores to use. The cross-validation loop will attempt to send different CV folds off to different cores. |
trace |
a logical value, print progress of cross validation or not |
parallel |
a logical value, parallel computing or not |
... |
Other arguments that can be passed to |
Details
The function runs nclreg
nfolds
+1 times; the
first to compute the lambda
sequence, and then to
compute the fit with each of the folds omitted. The error or the loss value is
accumulated, and the average value and standard deviation over the
folds is computed. Note that cv.nclreg
can be used to search for
values for alpha
: it is required to call cv.nclreg
with a fixed vector foldid
for different values of alpha
.
Value
an object of class "cv.nclreg"
is returned, which is a
list with the ingredients of the cross-validation fit.
fit |
a fitted nclreg object for the full data. |
residmat |
matrix of loss values or errors with row values for |
cv |
The mean cross-validated loss values or errors - a vector of length
|
cv.error |
estimate of standard error of |
foldid |
an optional vector of values between 1 and |
lambda |
a vector of |
lambda.which |
index of |
lambda.optim |
value of |
Author(s)
Zhu Wang <zwang145@uthsc.edu>
References
Zhu Wang (2021), MM for Penalized Estimation, TEST, doi: 10.1007/s11749-021-00770-2
See Also
nclreg
and plot
, predict
, and coef
methods for "cv.nclreg"
object.