Formula interface for elastic net cross-validation with cv.glmnet
cv.glmnet(x, ...)# S3 method for default
cv.glmnet(x, y, ...)
# S3 method for formula
cv.glmnet(formula, data, alpha = 1, nfolds = 10, ...,
weights = NULL, offset = NULL, subset = NULL,
na.action = getOption("na.action"), drop.unused.levels = FALSE,
xlev = NULL, sparse = FALSE, use.model.frame = FALSE,
gamma = c(0, 0.25, 0.5, 0.75, 1), relax = FALSE)
# S3 method for cv.glmnet.formula
predict(object, newdata, na.action = na.pass,
...)
# S3 method for cv.glmnet.formula
coef(object, ...)
# S3 method for cv.glmnet.formula
print(x, ...)
# S3 method for cv.relaxed.formula
predict(object, newdata,
na.action = na.pass, ...)
# S3 method for cv.glmnet.formula
coef(object, ...)
For the default method, a matrix of predictor variables.
For cv.glmnet.formula
and cv.glmnet.default
, other arguments to be passed to glmnet::cv.glmnet; for the predict
and coef
methods, arguments to be passed to their counterparts in package glmnet.
For the default method, a response vector or matrix (for a multinomial response).
A model formula; interaction terms are allowed and will be expanded per the usual rules for linear models.
A data frame or matrix containing the variables in the formula.
The elastic net mixing parameter. See glmnet::glmnet for more details.
The number of crossvalidation folds to use. See glmnet::cv.glmnet for more details.
An optional vector of case weights to be used in the fitting process. If missing, defaults to an unweighted fit.
An optional vector of offsets, an a priori known component to be included in the linear predictor.
An optional vector specifying the subset of observations to be used to fit the model.
A function which indicates what should happen when the data contains missing values. For the predict
method, na.action = na.pass
will predict missing values with NA
; na.omit
or na.exclude
will drop them.
Should factors have unused levels dropped? Defaults to FALSE
.
A named list of character vectors giving the full set of levels to be assumed for each factor.
Should the model matrix be in sparse format? This can save memory when dealing with many factor variables, each with many levels.
Should the base model.frame function be used when constructing the model matrix? This is the standard method that most R modelling functions use, but has some disadvantages. The default is to avoid model.frame
and construct the model matrix term-by-term; see discussion.
For cv.glmnet.formula
, the values of the parameter for mixing the relaxed (non-regularised) fit with the regularized fit. Not used if relax=FALSE
. Requires glmnet 3.0 or later.
For cv.glmnet.formula
, whether to perform a relaxed fit after the regularised one. Requires glmnet 3.0 or later.
For the predict
and coef
methods, an object of class cv.glmnet.formula
.
For the predict
method, a data frame containing the observations for which to calculate predictions.
For cv.glmnet.formula
, an object of class either cv.glmnet.formula
or cv.relaxed.formula
, based on the value of the relax
argument. This is basically the same object created by glmnet::cv.glmnet
, but with extra components to allow formula usage.
The cv.glmnet
function in this package is an S3 generic with a formula and a default method. The former calls the latter, and the latter is simply a direct call to the cv.glmnet
function in package glmnet
. All the arguments to glmnet::cv.glmnet
are (or should be) supported.
There are two ways in which the matrix of predictors can be generated. The default, with use.model.frame = FALSE
, is to process the additive terms in the formula independently. With wide datasets, this is much faster and more memory-efficient than the standard R approach which uses the model.frame
and model.matrix
functions. However, the resulting model object is not exactly the same as if the standard approach had been used; in particular, it lacks a bona fide terms object. If you require interoperability with other packages that assume the standard model object structure, set use.model.frame = TRUE
. See discussion for more information on this topic.
The predict
and coef
methods are wrappers for the corresponding methods in the glmnet package. The former constructs a predictor model matrix from its newdata
argument and passes that as the newx
argument to glmnet:::predict.cv.glmnet
.
glmnet::cv.glmnet, glmnet::predict.cv.glmnet, glmnet::coef.cv.glmnet, model.frame, model.matrix
# NOT RUN {
cv.glmnet(mpg ~ ., data=mtcars)
cv.glmnet(Species ~ ., data=iris, family="multinomial")
# }
# NOT RUN {
# Leukemia example dataset from Trevor Hastie's website
download.file("https://web.stanford.edu/~hastie/glmnet/glmnetData/Leukemia.RData",
"Leukemia.RData")
load("Leukemia.Rdata")
leuk <- do.call(data.frame, Leukemia)
cv.glmnet(y ~ ., leuk, family="binomial")
# }
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