Pearson's correlation for tidyfit — .model.cor • tidyfit

Calculates Pearson's correlation coefficient on a 'tidyFit' R6 class. The function can be used with regress.

Usage

# S3 method for cor
.model(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::cor.test. See ?cor.test for more details.

Implementation

Results can be viewed using coef.

Author

Johann Pfitzinger

Examples

# Load data
data <- tidyfit::Factor_Industry_Returns

# Stand-alone function
fit <- m("cor", 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::cor.test     0.00985 #0010000 <tidyFit>    <tibble [1 × 0]>

# Within 'regress' function
fit <- regress(data, Return ~ ., m("cor"), .mask = c("Date", "Industry"))
tidyr::unnest(coef(fit), model_info)
#> # A tibble: 6 × 10
#> # Groups:   model [1]
#>   model term   estimate statistic  p.value param…¹ conf.…² conf.h…³ method
#>   <chr> <chr>     <dbl>     <dbl>    <dbl>   <int>   <dbl>    <dbl> <chr> 
#> 1 cor   Mkt-RF   0.784     106.   0           7078  0.775   0.793   Pears…
#> 2 cor   SMB      0.210      18.1  2.86e-71    7078  0.187   0.232   Pears…
#> 3 cor   HML     -0.104      -8.79 1.83e-18    7078 -0.127  -0.0808  Pears…
#> 4 cor   RMW     -0.0957     -8.09 7.18e-16    7078 -0.119  -0.0725  Pears…
#> 5 cor   CMA     -0.240     -20.8  5.22e-93    7078 -0.261  -0.218   Pears…
#> 6 cor   RF      -0.0152     -1.28 2.00e- 1    7078 -0.0385  0.00808 Pears…
#> # … with 1 more variable: alternative <chr>, and abbreviated variable
#> #   names ¹parameter, ²conf.low, ³conf.high

Pearson's correlation for `tidyfit`