dot-fit.hfr: Hierarchical feature regression for 'tidyfit'

In tidyfit: Regularized Linear Modeling with Tidy Data

Hierarchical feature regression for tidyfit

Description

Fits a hierarchical feature regression on a 'tidyFit' R6 class. The function can be used with regress.

Usage

## S3 method for class 'hfr'
.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).

Details

Hyperparameters:

• kappa (proportional size of regression graph)

Important method arguments (passed to m)

The hierarchical feature regression is estimated using the hfr::cv.hfr function. See ?cv.hfr for more details.

Implementation

Features are standardized by default with coefficients transformed to the original scale.

If no hyperparameter grid is provided (is.null(control$kappa)), the default is seq(0, 1, by = 0.1).

Value

A fitted 'tidyFit' class model.

Author(s)

Johann Pfitzinger

References

Pfitzinger J (2022). hfr: Estimate Hierarchical Feature Regression Models. R package version 0.5.0, https://CRAN.R-project.org/package=hfr.

See Also

.fit.plsr and m methods

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

# Stand-alone function
fit <- m("hfr", Return ~ ., data, kappa = 0.5)
fit

# Within 'regress' function
fit <- regress(data, Return ~ ., m("hfr", kappa = c(0.1, 0.5)),
               .mask = c("Date", "Industry"))
coef(fit)

tidyfit documentation built on Oct. 3, 2024, 5:06 p.m.