effect_metrics {volker}

Output effect sizes and regression model parameters

Description

The regression type depends on the number of selected columns:

• One column: see effect_metrics_one (not yet implemented)

• Multiple columns: see effect_metrics_items

• One column and one grouping column: see effect_metrics_one_grouped

• Multiple columns and one grouping column: see effect_metrics_items_grouped (not yet implemented)

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:

• Two metric columns: see effect_metrics_one_cor

• Multiple columns: see effect_metrics_items_cor

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)