Generic cross-validation function

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

- 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

An object of class '`cross_validated`

' is returned. See
`cross_validated-class`

for more details about this class and
its generic functions.

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.

```
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
#> Call: cv(models = list(m0 = f0, m1 = f1, m2 = f2), data = iris, rep = 10,
#> formula = Sepal.Length ~ .)
#>
#> 5-fold cross-validation with 10 repetitions
#>
#> mse mae
#> m0 0.09955688 0.2553216
#> m1 0.27017313 0.4062745
#> m2 0.11754607 0.2769748
```