Bayesian model averaging for tidyfit

Fits a Bayesian model averaging regression on a 'tidyFit' R6 class. The function can be used with regress.

# S3 method for class 'bma'
.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)

iter (number of iteration draws)
mcmc (model sampler used (default 'bd'))

The function provides a wrapper for BMS::bms. See ?bms for more details.

Implementation

The underlying function automatically generates plotting output, which is not suppressed.

Use coef(fit) to obtain posterior mean, standard deviation as well as posterior inclusion probabilities for the features.

References

Feldkircher, M. and S. Zeugner (2015). Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R, Journal of Statistical Software 68(4).

Author

Johann Pfitzinger

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

# Stand-alone function
fit <- m("bma", Return ~ `Mkt-RF` + HML + SMB + RMW + CMA, data)
fit
#> # A tibble: 1 × 5
#>   estimator_fct `size (MB)` grid_id  model_object settings        
#>   <chr>               <dbl> <chr>    <list>       <list>          
#> 1 BMS::bms             3.20 #0010000 <tidyFit>    <tibble [1 × 1]>

# Within 'regress' function
fit <- regress(data, Return ~ ., m("bma"), .mask = c("Date", "Industry"))
coef(fit)
#> # A tibble: 7 × 4
#> # Groups:   model [1]
#>   model term        estimate model_info      
#>   <chr> <chr>          <dbl> <list>          
#> 1 bma   CMA          0.118   <tibble [1 × 2]>
#> 2 bma   HML          0.0584  <tibble [1 × 2]>
#> 3 bma   Mkt-RF       0.979   <tibble [1 × 2]>
#> 4 bma   RF           1.00    <tibble [1 × 2]>
#> 5 bma   RMW          0.158   <tibble [1 × 2]>
#> 6 bma   SMB          0.00255 <tibble [1 × 2]>
#> 7 bma   (Intercept) -0.0258  <tibble [1 × 2]>