targeted (version 0.5)

cv: Cross-validation

Description

Generic cross-validation function

Usage

cv(
  models,
  data,
  response = NULL,
  nfolds = 5,
  rep = 1,
  weights = NULL,
  modelscore,
  seed = NULL,
  shared = NULL,
  args.pred = NULL,
  args.future = list(),
  mc.cores,
  ...
)

Value

An object of class 'cross_validated' is returned. See cross_validated-class for more details about this class and its generic functions.

Arguments

models

List of fitting functions

data

data.frame or matrix

response

Response variable (vector or name of column in data).

nfolds

Number of folds (default 5. K=0 splits in 1:n/2, n/2:n with last part used for testing)

rep

Number of repetitions (default 1)

weights

Optional frequency weights

modelscore

Model scoring metric (default: MSE / Brier score). Must be a function with arguments: response, prediction, weights, ...

seed

Random seed (argument parsed to future_Apply::future_lapply)

shared

Function applied to each fold with results send to each model

args.pred

Optional arguments to prediction function (see details below)

args.future

Arguments to future.apply::future_mapply

mc.cores

Optional number of cores. parallel::mcmapply used instead of future

...

Additional arguments parsed to models in models

Author

Klaus K. Holst

Details

models should be list of objects of class ml_model. Alternatively, each element of models should be a list with a fitting function and a prediction function.

The response argument can optionally be a named list where the name is then used as the name of the response argument in models. Similarly, if data is a named list with a single data.frame/matrix then this name will be used as the name of the data/design matrix argument in models.

Examples

f0 <- function(data,...) lm(...,data=data)
f1 <- function(data,...) lm(Sepal.Length~Species,data=data)
f2 <- function(data,...) lm(Sepal.Length~Species+Petal.Length,data=data)
x <- cv(list(m0=f0,m1=f1,m2=f2),rep=10, data=iris, formula=Sepal.Length~.)
x