The type of effect size depends on the number of selected columns:
One categorical column: seeeffect_counts_one
Multiple categorical columns: seeeffect_counts_items
Cross tabulations:
One categorical column and one grouping column: seeeffect_counts_one_grouped
Multiple categorical columns and one grouping column: seeeffect_counts_items_grouped (not yet implemented)
Multiple categorical columns and multiple grouping columns:effect_counts_items_grouped_items (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:
One categorical column and one metric column: seeeffect_counts_one_cor (not yet implemented)
Multiple categorical columns and one metric column: seeeffect_counts_items_cor (not yet implemented)
Multiple categorical columns and multiple metric columns:effect_counts_items_cor_items (not yet implemented)
![[Experimental]](/image.pl?url=https%3a%2f%2fstrohne.github.io%2fvolker%2freference%2ffigures%2flifecycle-experimental.svg&f=jpg&w=240)
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. The column name 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 bydata_clean.
- ...
Other parameters passed to the appropriate effect function.
Examples
library(volker)data<-volker::chatgpteffect_counts(data,sd_gender,adopter)#>#>#> |sd_gender | adopter| n| p_x| p_y| p_xy| ratio| pmi| npmi| fisher_p| fisher_stars|#> |:---------|----------------------------------------:|--:|----:|----:|----:|-----:|-----:|-----:|--------:|------------:|#> |female | I try new offers immediately| 2| 0.40| 0.15| 0.02| 0.34| -1.57| -0.28| 0.142| |#> |female | I try new offers rather quickly| 25| 0.40| 0.62| 0.25| 1.00| 0.00| 0.00| 1.000| |#> |female | I wait until offers establish themselves| 13| 0.40| 0.22| 0.13| 1.49| 0.58| 0.20| 0.142| |#> |male | I try new offers immediately| 12| 0.59| 0.15| 0.12| 1.35| 0.43| 0.14| 0.187| |#> |male | I try new offers rather quickly| 38| 0.59| 0.62| 0.38| 1.02| 0.02| 0.02| 1.000| |#> |male | I wait until offers establish themselves| 9| 0.59| 0.22| 0.09| 0.69| -0.54| -0.15| 0.142| |#> |male | I only use new offers when I have no ...| 1| 0.59| 0.01| 0.01| 1.68| 0.75| 0.11| 1.000| |#> |diverse | I try new offers immediately| 1| 0.01| 0.15| 0.01| 6.73| 2.75| 0.41| 0.238| |#>#> Adjusted significance p values with fdr method.#>#>#>#> |Statistic | Value|#> |:-----------|-----:|#> |Cramer's V | 0.26|#> |Chi-squared | 13.48|#> |n | 101|#> |df | |#> |p | 0.019|#> |stars | *|