cv.intnet: Cross validation interaction screening

In jaredhuling/intscreen:

Description

This function implements CV interaction screening

Usage

cv.intnet(x, y, weights, offset = NULL, lambda = NULL,
  type.measure = c("mse", "deviance", "class", "auc", "mae"),
  nfolds = 10, foldid, grouped = TRUE, keep = FALSE,
  parallel = FALSE, modifier = NULL, ...)

Arguments

x	x matrix as in glmnet.
y	response y as in glmnet.
weights	Observation weights; defaults to 1 per observation
offset	Offset vector (matrix) as in glmnet
lambda	Optional user-supplied lambda sequence; default is NULL, and glmnet chooses its own sequence
type.measure	loss to use for cross-validation. Currently five options, not all available for all models. The default is type.measure="deviance", which uses squared-error for gaussian models (a.k.a type.measure="mse" there), deviance for logistic and poisson regression, and partial-likelihood for the Cox model. type.measure="class" applies to binomial and multinomial logistic regression only, and gives misclassification error. type.measure="auc" is for two-class logistic regression only, and gives area under the ROC curve. type.measure="mse" or type.measure="mae" (mean absolute error) can be used by all models except the "cox"; they measure the deviation from the fitted mean to the response.
nfolds	number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3
foldid	an optional vector of values between 1 and nfold identifying what fold each observation is in. If supplied, nfold can be missing.
grouped	This is an experimental argument, with default TRUE, and can be ignored by most users. For all models except the "cox", this refers to computing nfolds separate statistics, and then using their mean and estimated standard error to describe the CV curve. If grouped=FALSE, an error matrix is built up at the observation level from the predictions from the nfold fits, and then summarized (does not apply to type.measure="auc"). For the "cox" family, grouped=TRUE obtains the CV partial likelihood for the Kth fold by subtraction; by subtracting the log partial likelihood evaluated on the full dataset from that evaluated on the on the (K-1)/K dataset. This makes more efficient use of risk sets. With grouped=FALSE the log partial likelihood is computed only on the Kth fold
keep	If keep=TRUE, a prevalidated array is returned containing fitted values for each observation and each value of lambda. This means these fits are computed with this observation and the rest of its fold omitted. The folid vector is also returned. Default is keep=FALSE
parallel	If TRUE, use parallel foreach to fit each fold. Must register parallel before hand, such as doMC or others. See the example below.
modifier	effect modifier
...	other arguments to be passed to intnet

Examples

library(intscreen)

set.seed(1)
x <- matrix(rnorm(100 * 350), ncol = 350)
y <- rnorm(100) + x[,1] * x[,2] * 0.5 - x[,3] * x[,4] * 0.5


## require that each interaction be in the top 50 ints 100% of the 15 splits
intmod <- cv.intnet(x, y, nints = 50, nsplits = 15, fraction.in.thresh = 1)

plot(intmod)

cfs <- as.matrix(predict(intmod, type = "coef", s = "lambda.min"))
cfs[cfs != 0,,drop=FALSE]

jaredhuling/intscreen documentation built on May 19, 2019, 9:38 p.m.