Quantile regression for tidyfit

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

Usage

# S3 method for class 'quantile'
.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:

None. Cross validation not applicable.

Important method arguments (passed to m)

• tau (the quantile(s) to be estimated)

The function provides a wrapper for quantreg::rq. See ?rq for more details. The argument tau is the chosen quantile (default tau = 0.5).

Implementation

No implementation notes

References

Koenker R (2022). quantreg: Quantile Regression. R package version 5.94, https://CRAN.R-project.org/package=quantreg.

See also

.fit.lm, .fit.bayes and m methods

Author

Johann Pfitzinger

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

fit <- regress(data, Return ~ .,
               m("quantile", tau = c(0.1, 0.5, 0.9)),
               .mask = c("Date", "Industry"))
coef(fit)
#> # A tibble: 21 × 4
#> # Groups:   model [1]
#>    model    term        estimate model_info      
#>    <chr>    <chr>          <dbl> <list>          
#>  1 quantile (Intercept) -3.50    <tibble [1 × 3]>
#>  2 quantile Mkt-RF       0.942   <tibble [1 × 3]>
#>  3 quantile SMB         -0.00520 <tibble [1 × 3]>
#>  4 quantile HML          0.0928  <tibble [1 × 3]>
#>  5 quantile RMW          0.132   <tibble [1 × 3]>
#>  6 quantile CMA          0.00170 <tibble [1 × 3]>
#>  7 quantile RF           0.677   <tibble [1 × 3]>
#>  8 quantile (Intercept) -0.0937  <tibble [1 × 3]>
#>  9 quantile Mkt-RF       0.984   <tibble [1 × 3]>
#> 10 quantile SMB          0.0373  <tibble [1 × 3]>
#> # ℹ 11 more rows