Cross validation for hd2part models

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)