Quantile regression forest for tidyfit — .fit.quantile

Fits a nonlinear quantile regression forest on a 'tidyFit' R6 class. The function can be used with regress.

Usage

# S3 method for class 'quantile_rf'
.fit(self, data = NULL)

Arguments

self: a 'tidyFit' R6 class.
data: a data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).

Value

A fitted 'tidyFit' class model.

Details

Hyperparameters:

ntree (number of trees)
mtry (number of variables randomly sampled at each split)

Important method arguments (passed to m)

tau (the quantile(s) to be estimated)

The function provides a wrapper for quantregForest::quantregForest. See ?quantregForest for more details. The argument tau is the chosen quantile (default tau = 0.5). tau is passed directly to m('quantile_rf', tau = c(0.1, 0.5, 0.9) and is not passed to predict as in the quantregForest::quantregForest package. This is done to ensure a consistent interface with the quantile regression from quantreg.

Implementation

No implementation notes

References

Meinshausen N (2017). quantregForest: Quantile Regression Forests. R package version 1.3-7, https://CRAN.R-project.org/package=quantregForest.

Author

Johann Pfitzinger

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns
data <- dplyr::filter(data, Industry == "HiTec")
data <- dplyr::select(data, -Date, -Industry)

# Stand-alone function
fit <- m("quantile_rf", Return ~ ., data, tau = 0.5, ntree = 50)
fit
#> # A tibble: 1 × 5
#>   estimator_fct                  `size (MB)` grid_id model_object settings
#>   <chr>                                <dbl> <chr>   <list>       <list>  
#> 1 quantregForest::quantregForest        1.26 #00100… <tidyFit>    <tibble>

# Within 'regress' function
fit <- regress(data, Return ~ .,
               m("quantile_rf", tau = c(0.1, 0.5, 0.9), ntree = 50))
explain(fit)
#> Warning: using explain package 'randomForest'
#> # A tibble: 7 × 5
#> # Groups:   model [1]
#>   model       term        importance IncNodePurity importanceSD
#>   <chr>       <chr>            <dbl>         <dbl>        <dbl>
#> 1 quantile_rf (Intercept)      0                0         0    
#> 2 quantile_rf Mkt-RF          37.5          14685.        1.56 
#> 3 quantile_rf SMB              0.500         2104.        0.354
#> 4 quantile_rf HML              3.83          3022.        0.638
#> 5 quantile_rf RMW              1.96          2543.        0.316
#> 6 quantile_rf CMA              4.19          4502.        0.743
#> 7 quantile_rf RF               0.690         1306.        0.226

Quantile regression forest for `tidyfit`