cv.hd2part {personalized2part}

Cross validation for hd2part models

Description

Cross validation for hd2part models

Usage

cv.hd2part(
  x,
  z,
  x_s,
  s,
  weights = rep(1, NROW(x)),
  weights_s = rep(1, NROW(x_s)),
  offset = NULL,
  offset_s = NULL,
  lambda = NULL,
  type.measure = c("mae", "mse", "sep-auc-mse", "sep-auc-mae"),
  nfolds = 10,
  foldid = NULL,
  grouped = TRUE,
  keep = FALSE,
  parallel = FALSE,
  ...
)

Arguments

x

an n x p matrix of covariates for the zero part data, where each row is an observation and each column is a predictor. MUST be ordered such that the first n_s rows align with the observations in x_s and s

z

a length n vector of responses taking values 1 and 0, where 1 indicates the response is positive and zero indicates the response has value 0. MUST be ordered such that the first n_s values align with the observations in x_s and s

x_s

an n_s x p matrix of covariates (which is a submatrix of x) for the positive part data, where each row is an observation and each column is a predictor

s

a length n_s vector of responses taking strictly positive values

weights

a length n vector of observation weights for the zero part data

weights_s

a length n_s vector of observation weights for the positive part data

offset

a length n vector of offset terms for the zero part data

offset_s

a length n_s vector of offset terms for the positive part data

lambda

A user supplied lambda sequence. By default, the program computes its own lambda sequence based on nlambda and lambda.min.ratio. Supplying a value of lambda overrides this.

type.measure

measure to evaluate for cross-validation. Will add more description later

nfolds

number of folds for cross-validation. default is 10. 3 is smallest value allowed.

foldid

an optional vector of values between 1 and nfold specifying which fold each observation belongs to.

grouped

Like in glmnet, this is an experimental argument, with default TRUE, and can be ignored by most users. For all models, 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").

keep

If keep = TRUE, a prevalidated list of arrasy is returned containing fitted values for each observation and each value of lambda for each model. 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.

...

other parameters to be passed to hd2part function

Examples

set.seed(1)