cv: Cross-validation

In targeted: Targeted Inference

Cross-validation

Description

Generic cross-validation function

Usage

cv(
  models,
  data,
  response = NULL,
  K = 5,
  rep = 1,
  weights = NULL,
  modelscore,
  seed = NULL,
  shared = NULL,
  args.pred = NULL,
  ...
)

Arguments

models	List of fitting functions
data	data.frame or matrix
response	Response variable (vector or name of column in data).##'
K	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: RMSE / 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)
...	Additional arguments parsed to models in models

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 reponse 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.

Value

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

Author(s)

Klaus K. Holst

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

targeted documentation built on Oct. 26, 2022, 1:09 a.m.