Generalized linear regression for tidyfit

Fits a linear or logistic regression on a 'tidyFit' R6 class. The function can be used with regress and classify.

Usage

# S3 method for class 'glm'
.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)

The function provides a wrapper for stats::glm. See ?glm for more details.

Implementation

No implementation notes

Author

Johann Pfitzinger

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns
data$Return <- ifelse(data$Return > 0, 1, 0)

# Stand-alone function
fit <- m("glm", Return ~ ., data)
fit
#> # A tibble: 1 × 5
#>   estimator_fct `size (MB)` grid_id  model_object settings        
#>   <chr>               <dbl> <chr>    <list>       <list>          
#> 1 stats::glm           5.44 #0010000 <tidyFit>    <tibble [1 × 1]>

# Within 'classify' function
fit <- classify(data, Return ~ ., m("glm"), .mask = c("Date", "Industry"))
coef(fit)
#> # A tibble: 7 × 4
#> # Groups:   model [1]
#>   model term        estimate model_info      
#>   <chr> <chr>          <dbl> <list>          
#> 1 glm   (Intercept)   0.0363 <tibble [1 × 3]>
#> 2 glm   Mkt-RF        0.584  <tibble [1 × 3]>
#> 3 glm   SMB           0.0211 <tibble [1 × 3]>
#> 4 glm   HML           0.0361 <tibble [1 × 3]>
#> 5 glm   RMW           0.155  <tibble [1 × 3]>
#> 6 glm   CMA           0.104  <tibble [1 × 3]>
#> 7 glm   RF            0.332  <tibble [1 × 3]>

Generalized linear regression for `tidyfit`