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The calculations depend on the number of selected columns:

Group comparisons:

By default, if you provide two column selections, the second column is treated as categorical. Setting the metric-parameter to TRUE will call the appropriate functions for correlation analysis:

[Experimental]

Usage

effect_metrics(data, cols, cross = NULL, metric = FALSE, clean = TRUE, ...)

Arguments

data

A data frame.

cols

A tidy column selection, e.g. a single column (without quotes) or multiple columns selected by methods such as starts_with().

cross

Optional, a grouping column (without quotes).

metric

When crossing variables, the cross column parameter can contain categorical or metric values. By default, the cross column selection is treated as categorical data. Set metric to TRUE, to treat it as metric and calculate correlations.

clean

Prepare data by data_clean.

...

Other parameters passed to the appropriate effect function.

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics(data, sd_age, sd_gender)
#> 
#> 
#> |Term               | estimate| ci low| ci high| standard error|     t|     p| stars|
#> |:------------------|--------:|------:|-------:|--------------:|-----:|-----:|-----:|
#> |(Intercept)        |    37.52|  33.21|   41.84|           2.18| 17.24| 0.000|   ***|
#> |female (Reference) |         |       |        |               |      |      |      |
#> |male               |     3.69|  -1.88|    9.27|           2.81|  1.31| 0.192|      |
#> |diverse            |    -4.53| -32.18|   23.13|          13.94| -0.32| 0.746|      |
#> 
#> 
#> |Statistic                     | value|
#> |:-----------------------------|-----:|
#> |Adjusted R squared            |     0|
#> |Degrees of freedom            |     2|
#> |Residuals' degrees of freedom |    98|
#> |F                             |  0.98|
#> |p                             |  0.38|
#> |stars                         |      |