dsldConditDisparity: dsldConditDisparity

In dsld: Data Science Looks at Discrimination

dsldConditDisparity

Description

Plots (estimated) mean Y against X, separately for each level of S, with restrictions condits. May reveal Simpson's Paradox-like differences not seen in merely plotting mean Y against X.

Usage

dsldConditDisparity(data, yName, sName, xName, condits = NULL,
    qeFtn = qeKNN, minS = 50, useLoess = TRUE)

Arguments

data	Data frame or equivalent.
yName	Name of predicted variable Y. Must be numeric or dichtomous R factor.
sName	Name of the sensitive variable S, an R factor
xName	Name of a numeric column for the X-axis.
condits	An R vector; each component is a character string for an R logical expression representing a desired condition involving names(data) other than S and Y.
qeFtn	qeML predictive function (not quoted; only default arguments will be used.)
minS	Minimum size for an S group to be retained in the analysis.
useLoess	If TRUE, do loess smoothing on the fitted regression values.

Value

No value; plot.

Author(s)

N. Matloff, A. Ashok, S. Martha, A. Mittal

Examples

data(compas)
# graph probability of recidivism by race given age, among those with at
# most 4 prior convictions and COMPAS decile score at least 6
compas$two_year_recid <- as.numeric(compas$two_year_recid == "Yes")
dsldConditDisparity(compas,"two_year_recid", "race", "age", 
    c("priors_count <= 4","decile_score>=6"), qeKNN)

dsldConditDisparity(compas,"two_year_recid", "race", "age",
    "priors_count == 0", qeGBoost)

dsld documentation built on Sept. 14, 2024, 1:08 a.m.