Robust regression for tidyfit — .fit.robust • tidyfit

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).

Value

A fitted 'tidyFit' class model.

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.

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/.

Author

Johann Pfitzinger

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

fit <- regress(data, Return ~ ., m("robust"), .mask = c("Date", "Industry"))
coef(fit)
#> # A tibble: 7 × 4
#> # Groups:   model [1]
#>   model  term        estimate model_info      
#>   <chr>  <chr>          <dbl> <list>          
#> 1 robust (Intercept)  -0.0999 <tibble [1 × 2]>
#> 2 robust Mkt-RF        0.977  <tibble [1 × 2]>
#> 3 robust SMB           0.0283 <tibble [1 × 2]>
#> 4 robust HML           0.0485 <tibble [1 × 2]>
#> 5 robust RMW           0.193  <tibble [1 × 2]>
#> 6 robust CMA           0.119  <tibble [1 × 2]>
#> 7 robust RF            1.07   <tibble [1 × 2]>

# With robust standard errors
fit <- m("robust", Return ~ `Mkt-RF` + HML + SMB, data, vcov. = "HAC")
tidyr::unnest(coef(fit), model_info)
#> # A tibble: 4 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   0.380     0.0377    10.1   5.89e-24
#> 2 Mkt-RF        0.951     0.0126    75.2   0       
#> 3 HML           0.0971    0.0255     3.81  1.40e- 4
#> 4 SMB          -0.0112    0.0196    -0.572 5.67e- 1

Robust regression for `tidyfit`