SCAtools

Sufficient Condition Analysis (SCA) in R.

What the method is

A condition is sufficient for an outcome when having the condition guarantees the outcome. In data, a sufficiency claim makes a definite prediction: the cases that would contradict it do not occur. Plot the condition X against the outcome Y and those cases sit in one corner of the scatter plot. “High X is sufficient for high Y” predicts that the lower-right corner – high X with low Y – is empty.

Sufficient Condition Analysis estimates that empty corner. It fits the boundary of the empty space, measures how large the empty space is, tests whether it is larger than random pairing of X and Y would produce, and turns the boundary into thresholds: the level of X that guarantees each level of Y.

A sufficiency claim can run in four directions, and each predicts a different empty corner:

Direction	Claim	Empty corner
`HH`	high X is sufficient for high Y	lower-right
`LH`	low X is sufficient for high Y	lower-left
`HL`	high X is sufficient for low Y	upper-right
`LL`	low X is sufficient for low Y	upper-left

You state the claim; the package finds the corner.

What it is for

Finding the level that guarantees an outcome. The threshold table answers “how much X is enough for Y to reach this level?” for every outcome level, in the units of the data or as percentages, percentiles or standard deviations.
Seeing what regression does not show. A regression slope describes how the outcome moves on average. A condition can leave the average almost unchanged and still rule out low outcomes completely. SCA measures that second kind of effect, and sca_compare() reports both side by side, so a study can say which of the two its data show.
Comparing conditions. Several conditions can be analysed in one call, each with its own direction, and reported in one table.
Planning a study. sca_power() estimates how many observations a sufficiency effect of a given size needs.

Installation

install.packages("SCAtools")

How to use it

The package includes four simulated datasets that show the four ways a regression effect and a sufficiency effect can combine.

library(SCAtools)

# Configuration II: a flat regression slope, but an empty lower-right corner
dat <- subset(sca_four_configurations, configuration == "II")

# 1. Fit the claim "high X is sufficient for high Y"
fit <- sca_analysis(dat, x = "X", y = "Y", direction = "HH",
                    scope = c(0, 100, 0, 100), test.rep = 1000)
fit                     # summary, with every setting the result rests on

# 2. Read the results
sca_table(fit)          # effect size, p value and support for each condition
sca_thresholds(fit)     # the X level that guarantees each Y level
sca_plot(fit)           # the scatter plot, the frontier and the empty corner

# 3. Set it beside regression
sca_compare(fit)        # here: slope near zero, large sufficiency effect

sca() runs the same analysis with a permutation test and draws the plot in one step:

sca(dat, x = "X", y = "Y", direction = "HH")

Checking the result

Function	What it shows
`sca_test_plot()`	the permutation distribution beside the observed effect size
`sca_scope()`	the scope each effect size was divided by
`sca_scope_sensitivity()`	how the effect size changes with the scope
`sca_resample()`	how much the effect size varies across subsamples
`sca_outliers()`	which observations hold up the frontier
`sca_locationshift()`	whether the frontier is a level shift or a threshold
`sca_magnitude()`	conventional bands for an effect size

Reading the results

Effect size is the empty area as a share of the scope, from 0 (no empty corner) to 1. effect_size_null beside it is the value random pairing alone produces with the same data.
p value comes from a permutation test that breaks the pairing of X and Y.
Thresholds read as rules such as X > 38.9 => Y > 30. no_threshold marks an outcome level that no value of X reaches; always_satisfied marks one that every observation in scope already meets.
Scales. A fitted model can be re-expressed without refitting:
```
sca_thresholds(fit, scale = "actual")      # original units
sca_thresholds(fit, scale = "percentile")  # percentiles
sca_thresholds(fit, scale = "sd")          # standard deviations
```
Under the default absolute convention 0 is the low end of every axis and the inequality carries the direction; under directional a low-level axis is mirrored so that every rule reads as “more of the sufficient condition”. sca_scales() lists the options.

Good practice

Choose the direction from theory, then plot the data. Each direction predicts a single empty corner, which assumes the relationship is monotone.
Report the scope. The effect size is an area divided by the scope, so state whether the scope is the observed range or a theoretical one, and why. sca_scope() and the printed Settings block record it.
Report the frontier technique and the number of permutations. CE-FDH ("ce_fdh") follows the data as a step function; CR-FDH ("cr_fdh") fits a straight line through it. 1000 or more permutations are usual.
Fix a relevance threshold in advance with relevance = if a minimum effect size matters for the conclusion.
Report thresholds as empirical rules for the population and design studied; how far they generalise depends on the design.

An ordinary least-squares or median regression line can be added with reference = "ols" or "qr". It is drawn for comparison and reported by sca_reference(); it is not part of the sufficiency analysis.

Learn more

?SCAtools describes the method and gives its references.
citation("SCAtools") gives the references to cite.
system.file("docs", "sufficiency-logic.md", package = "SCAtools") derives the four directions step by step, and system.file("examples", "four-directions.R", package = "SCAtools") runs them.
The NSCA package uses SCAtools to test conditions that are both necessary and sufficient.

Source and issue tracker: https://github.com/youngchanresearcher/SCAtools

The Traditional Chinese version of this README is installed with the package:

file.show(system.file("docs", "README_zh-TW.md", package = "SCAtools"))