volker (version 2.1.0)

effect_metrics: Output effect sizes and test statistics for metric data

Description

The calculations depend on the number of selected columns:

• One metric column: see effect_metrics_one

• Multiple metric columns: see effect_metrics_items

Group comparisons:

• One metric column and one grouping column: see effect_metrics_one_grouped

• Multiple metric columns and one grouping column: see effect_metrics_items_grouped

• Multiple metric columns and multiple grouping columns: 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 metric columns and one metric column: see effect_metrics_items_cor

• Two metric column selections: see effect_metrics_items_cor_items

Usage

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

Value

A volker tibble.

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.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics(data, sd_age, sd_gender)