Fits a linear regression on a 'tidyFit' R6 class. The function can be used with regress.
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)
The function provides a wrapper for stats::lm. See ?lm 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.
See also
.fit.robust, .fit.glm and m methods
Examples
# Load data
data <- tidyfit::Factor_Industry_Returns
# Stand-alone function
fit <- m("lm", Return ~ `Mkt-RF` + HML + SMB, data)
fit
#> # A tibble: 1 × 5
#> estimator_fct `size (MB)` grid_id model_object settings
#> <chr> <dbl> <chr> <list> <list>
#> 1 stats::lm 2.77 #0010000 <tidyFit> <tibble [1 × 0]>
# Within 'regress' function
fit <- regress(data, Return ~ ., m("lm"), .mask = c("Date", "Industry"))
coef(fit)
#> # A tibble: 7 × 4
#> # Groups: model [1]
#> model term estimate model_info
#> <chr> <chr> <dbl> <list>
#> 1 lm (Intercept) -0.0312 <tibble [1 × 3]>
#> 2 lm Mkt-RF 0.977 <tibble [1 × 3]>
#> 3 lm SMB 0.0182 <tibble [1 × 3]>
#> 4 lm HML 0.0605 <tibble [1 × 3]>
#> 5 lm RMW 0.164 <tibble [1 × 3]>
#> 6 lm CMA 0.117 <tibble [1 × 3]>
#> 7 lm RF 1.00 <tibble [1 × 3]>
# With robust standard errors
fit <- m("lm", Return ~ `Mkt-RF` + HML + SMB, data, vcov. = "HAC")
fit
#> # A tibble: 1 × 5
#> estimator_fct `size (MB)` grid_id model_object settings
#> <chr> <dbl> <chr> <list> <list>
#> 1 stats::lm 2.77 #0010000 <tidyFit> <tibble [1 × 1]>