Combine several fitted models in a multimodel

The function models() is a more general version of c.model(). While the latter expects arguments inheriting of classes “model” or “multimodel” in its “...” arguments, the former also accepts fitted models (e.g. an “lm”).

Usage

models(..., .env = parent.frame())

Arguments

...

Passed to c.model(), after application of model() to fitted models in the list of “...” arguments.

.env

For internal use.

Value

A multimodel.

Examples

mm <- models(
  lm = lm(Sepal.Length ~ ., iris),
  rpart = model(rpart::rpart(Sepal.Length ~ ., iris)),
  xgboost = tune(fm_xgb(Sepal.Length ~ ., iris))
)
#> set_pref_iter(), model ‘model’, modifications made in call:
#>   pref_iter=14, nrounds=14, early_stopping_rounds=NULL
mm
#> --- A “multimodel” object containing 3 models ---
#> 
#> ‘lm’:
#>   model class:  lm
#>   formula:      Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + 
#>                     Species
#>   data:         data.frame [150 x 5], 
#>                 input as: ‘data = iris’
#>   call:         lm(formula = Sepal.Length ~ ., data = data)
#> 
#> ‘rpart’:
#>   model class:  rpart
#>   formula:      Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + 
#>                     Species
#>   data:         data.frame [150 x 5], 
#>                 input as: ‘data = iris’
#>   call:         rpart::rpart(formula = Sepal.Length ~ ., data = data)
#> 
#> ‘xgboost’:
#>   model class:  fm_xgb
#>   formula:      Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width + 
#>                     Species - 1
#>   data:         data.frame [150 x 5], 
#>                 input as: ‘data = iris’
#>   call:         fm_xgb(formula = Sepal.Length ~ ., data = data, 
#>                     nrounds = 14L, early_stopping_rounds = NULL, 
#>                     pref_iter = 14L)
cv_performance(mm)
#> --- Performance table ---
#> Metric: rmse
#>         train_rmse test_rmse iteration time_cv
#> lm         0.29963   0.31478        NA   0.016
#> rpart      0.31432   0.39094        NA   0.029
#> xgboost    0.18173   0.32602        14   0.064