Graphical and tabular operations
Conditional disparity
dsldConditDisparity() plots an outcome against a numeric
predictor, separately by sensitive group. It can restrict rows through
condits and omit groups with fewer than minS
rows. The binary plotting interface uses the first outcome factor level
as its positive class.
law_sample <- lsa[sample.int(nrow(lsa), 1500), ]
levels(law_sample$bar)
#> [1] "FALSE" "TRUE"
dsldConditDisparity(law_sample, "bar", "race1", "lsat", minS = 50)
Inspect the outcome coding, group sample sizes, and uncertainty before drawing substantive conclusions from the curves.
Group frequencies and possible confounders
The bundled svcensus data describe workers in
engineering occupations in Silicon Valley. Compare education and
occupation distributions by gender:
data(svcensus, package = "dsld")
names(svcensus)
#> [1] "age" "educ" "occ" "wageinc" "wkswrkd" "gender"
dsldFrequencyByS(svcensus, cName = "educ", sName = "gender")
#> Frequency of 14 Frequency of 16 Frequency of zzzOther
#> female 0.2068052 0.02098615 0.7722086
#> male 0.2177579 0.04110130 0.7411408
dsldFrequencyByS(svcensus, cName = "occ", sName = "gender")
#> Frequency of 100 Frequency of 101 Frequency of 102 Frequency of 106
#> female 0.3117359 0.2349226 0.3274246 0.04258354
#> male 0.2016862 0.2203267 0.3433671 0.01923330
#> Frequency of 140 Frequency of 141
#> female 0.02587612 0.05745721
#> male 0.04446055 0.17092610
tapply(svcensus$wageinc, svcensus$occ, mean)
#> 100 101 102 106 140 141
#> 50396.47 51373.53 68797.72 53639.86 67019.26 69494.44Columns follow the factor levels shown in the table labels. Education codes 14 and 16 represent master’s and doctoral degrees. Differences in occupation frequencies and mean wages can motivate examining occupation as a possible confounder. Selecting adjustment variables still requires substantive reasoning; similar group frequencies alone do not rule a variable in or out.
dsldDensityByS() compares numeric distributions.
dsldConfounders() applies frequency or density comparisons
to the other columns in a data frame.
Frequency-based parallel coordinates
Parallel coordinates place each variable on a separate vertical axis
and join the values belonging to one observation.
dsldFreqPCoord() can display the most typical patterns
within each sensitive group, using nearest-neighbor density estimates.
The axes are centered and scaled.
plot_data <- law_sample[, c("fam_inc", "ugpa", "gender", "lsat", "race1")]
dsldFreqPCoord(plot_data, m = 25, sName = "race1", k = 15)
This view can reveal multivariate patterns that separate plots miss. Its density and sampling settings affect which observations appear; it does not summarize every member of a group.
Comparing feature sets
dsldTakeALookAround() compares predictor subsets C. It
reports:
- a: prediction error for Y using C.
- b: prediction error for Y using C and S.
- c: prediction error for S using C.
The same held-out rows are used for all feature sets. Lower (c) means S is more predictable from C, which can indicate stronger proxy information. Interpret these errors relative to suitable outcome baselines.
wages <- svcensus[sample.int(nrow(svcensus), 800),
c("age", "educ", "wkswrkd", "gender", "wageinc")]
dsldTakeALookAround(wages, "wageinc", "gender", maxFeatureSetSize = 2, holdout = 100)
#> Warning in `[<-.factor`(`*tmp*`, ri, value = 3L): invalid factor level, NA
#> generated
#> Warning in `[<-.factor`(`*tmp*`, ri, value = 3L): invalid factor level, NA
#> generated
#> Feature Names a b c
#> 1 age 31517.44 30329.07 0.3
#> 2 educ 30587.71 29712.81 0.3
#> 3 wkswrkd 26729.35 26384.64 0.3
#> 4 age,educ 30126.36 29465.02 0.3
#> 5 age,wkswrkd 26667.38 26223.17 0.3
#> 6 educ,wkswrkd 27085.18 26776.01 0.3