.fit.robust {tidyfit}

Robust regression for tidyfit

Description

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

Usage

## S3 method for class 'robust'
.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:

None. Cross validation not applicable.

Important method arguments (passed to m)

• method (estimation algorithm, e.g. 'M', 'MM')

The function provides a wrapper for MASS::rlm. See ?rlm for more details.

Implementation“

An argument vcov. can be passed in control or to ... in m to estimate the model with robust standard errors. vcov. can be one of "BS", "HAC", "HC" and "OPG" and is passed to the sandwich package.

Value

A fitted 'tidyFit' class model.

Author(s)

Johann Pfitzinger

References

W. N. Venables and B. D. Ripley (2002). Modern Applied Statistics with S. 4th ed., Springer, New York. URL https://www.stats.ox.ac.uk/pub/MASS4/.

See Also

.fit.lm and m methods

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

fit <- regress(data, Return ~ ., m("robust"), .mask = c("Date", "Industry"))
coef(fit)

# With robust standard errors
fit <- m("robust", Return ~ `Mkt-RF` + HML + SMB, data, vcov. = "HAC")
tidyr::unnest(coef(fit), model_info)