tpac_compare {esvis}

Compare Transformed Proportion Above the Cut

Description

This function compares all possible pairwise comparisons, as supplied by formula, in terms of the transformed proportion above the cut. This is an effect-size like measure of the differences between two groups as the cut point(s) in the distribution. See Ho & Reardon, 2012

Usage

tpac_compare(data, formula, cuts, ref_group = NULL)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the type out ~ group where out is the outcome variable and group is the grouping variable. Note this variable can include any arbitrary number of groups. Additional variables can be included with + to produce separate estimates by the secondary or tertiary variables of interest (e.g., out ~ group + characteristic1 + characteristic2).

cuts

Optional vector of cut scores. If supplied, the ECDF will be guaranteed to include these points. Otherwise, there could be gaps in the ECDF at those particular points (used in plotting the cut scores).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using the formula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g., ref_group = ~ Active + `Non-FRL`, or ref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

Value

Tidy data frame of the proportion above the cutoff for each (or selected) groups.

See Also

[esvis::pac(), esvis::pac_diff(), esvis::tpac()]

Examples

# Compute differences for all pairwise comparisons for each of three cuts
tpac_compare(star,
    reading ~ condition, 
		 cut = c(450, 500, 550)) 
		 
tpac_compare(star,
    reading ~ condition + freelunch + race, 
		 cut = c(450, 500))

tpac_compare(star,
    reading ~ condition + freelunch + race, 
		 cut = c(450, 500),
		 ref_group = ~small + no + white)