ThSQCA for beginners, part 4: reporting and checking your results

Turn a threshold sweep into a report: generate_report(), configuration charts, a check against the QCA package, and a short checklist for reporting.
R
ThSQCA
tutorial
Author

Yuki Toyoda

Published

October 5, 2026

In the first three parts we ran sweeps and learned to read them. In this last part we turn a sweep into something you can report and defend: a written record of what was explored, a chart of the configurations, and a check that the numbers agree with the QCA package.

library(ThSQCA)
library(QCA)
Loading required package: admisc

To cite package QCA in publications, please use:
  Dusa, Adrian (2019) QCA with R. A Comprehensive Resource.
  Springer International Publishing.
Practice data (identical to part 1) 
make_demo_data <- function(n = 400, seed = 2026) {
  set.seed(seed)
  clip <- function(x) pmin(10, pmax(0, round(x)))
  QUA <- clip(rnorm(n, 6, 2))
  SER <- clip(rnorm(n, 6, 2))
  ENV <- clip(rnorm(n, 6, 2))
  core <- pmax(pmin(QUA, SER), pmin(QUA, ENV) - 1)
  LOY  <- clip(core + rnorm(n, 0, 0.7))
  data.frame(LOY, QUA, SER, ENV)
}

demo <- make_demo_data()

We reuse the outcome threshold sweep from part 1. This time we ask for the full details, which the report needs.

res_ots <- otSweep(
  dat            = demo,
  outcome        = "LOY",
  conditions     = c("QUA", "SER", "ENV"),
  sweep_range    = 5:8,
  thrX           = c(QUA = 7, SER = 7, ENV = 7),
  incl.cut       = 0.8,
  n.cut          = 1,
  return_details = TRUE
)

A report that records the whole sweep

A sweep produces many results, and a reader needs to know what range was explored, under which settings, and what came out at each threshold. generate_report() writes this into a Markdown file. It comes in two formats.

  • "simple" is a short report for the main text or an appendix. It has a summary table, the solution at each threshold, and code for checking the result with the QCA package.
  • "full" is for supplementary material. It adds an overview of the analysis (the sweep range and the cutoffs), descriptive statistics, and, for every threshold, the truth table, the solution fit, per-term metrics, a configuration chart, the QCA package output, and the settings needed to reproduce it. A comparison across thresholds follows.
report_file <- tempfile(fileext = ".md")
generate_report(res_ots, report_file, dat = demo, format = "simple")

Here are the first lines of the file it wrote. On your own computer, give the file a real name, such as "ots_report_simple.md", and it will appear in your working folder.

cat(head(readLines(report_file), 25), sep = "\n")
# QCA Analysis Report

*(Auto-generated by ThSQCA package)*

**Generated**: 2026-10-08 23:37

---

## Summary

| thrY | expression | inclS | covS | n_solutions |
|---|---|---|---|---|
| 5 | QUA\*SER + QUA\*ENV | 0.981 | 0.403 | 1 |
| 6 | QUA\*SER + QUA\*ENV | 0.926 | 0.565 | 1 |
| 7 | QUA\*SER | 0.894 | 0.656 | 1 |
| 8 | No solution | NA | NA | 0 |


---

## Solutions Overview

### LOY >= 5

**Solution**: QUA\*SER + QUA\*ENV -> LOY

A few options are worth knowing:

generate_report(res_ots, "r.md", dat = demo, format = "full")              # more detail
generate_report(res_ots, "r.md", dat = demo, include_chart = FALSE)        # no charts
generate_report(res_ots, "r.md", dat = demo, chart_symbol_set = "latex")   # LaTeX symbols
generate_report(res_ots, "r.md", dat = demo, include_raw_output = FALSE)   # omit QCA output

Configuration charts

QCA papers often show solutions as a chart: one column per path, one row per condition, a filled circle for “present”, a crossed circle for “absent”, and a blank where the path does not restrict the condition (neither presence nor absence is required). You can draw one from a list of paths with config_chart_from_paths().

paths <- c("A*B*~C", "A*D", "B*E")
cat(config_chart_from_paths(paths))
Condition T1 T2 T3
A ● ●
B ● ●
C ⊗
D ●
E ●

● = presence, ⊗ = absence, blank = don’t care

If you prefer plain characters, for example for a journal that cannot print the circle symbols, use the ASCII version:

cat(config_chart_from_paths(paths, symbol_set = "ascii"))
Condition T1 T2 T3
A O O
B O O
C X
D O
E O

O = presence, X = absence, blank = don’t care

How many cases stand behind each threshold?

A sweep table gives solutions and fit values, but not the number of cases behind them. A reader should be able to see that, because a result that rests on a handful of cases deserves less trust. For the outcome sweep of part 1, we can count the customers directly from the data, without ThSQCA.

thr <- 5:8
n_out  <- sapply(thr, function(t) sum(demo$LOY >= t))
n_both <- sapply(thr, function(t) sum(demo$LOY >= t & demo$QUA >= 7 & demo$SER >= 7))
cases <- rbind("High loyalty (LOY >= thrY)" = n_out,
               "... and high on QUA and SER" = n_both)
colnames(cases) <- thr

bp <- barplot(cases, beside = TRUE, col = c("#233A9A", "#F28018"),
              ylim = c(0, max(cases) * 1.15),
              xlab = "Outcome threshold (thrY)", ylab = "Number of customers",
              main = "Cases behind each threshold",
              legend.text = rownames(cases),
              args.legend = list(x = "topright", bty = "n", cex = 0.8))
text(bp, cases, labels = cases, pos = 3, cex = 0.8)

Grouped bar chart. At outcome threshold 5, there are 263 customers with high loyalty and 66 of them are high on quality and service. At 6 the counts are 177 and 65, at 7 they are 90 and 59, and at 8 they are 29 and 26.

Number of customers with high loyalty at each outcome threshold (blue), and how many of them are also high on both quality and service (orange).

As the target rises, the high-loyalty group shrinks from 263 customers to 29, so the solutions at the strict end of a sweep are built on far fewer cases. Reporting these counts next to the sweep table is a simple way to show how much each row can bear.

Check a cell against the QCA package

ThSQCA does not invent new results. Every cell is an ordinary QCA analysis, so you can reproduce any cell by hand. This is a good way to build trust in the tool, and to answer a reviewer who asks. Here we reproduce the cell at an outcome threshold of 7.

bin <- function(x, t) as.integer(x >= t)

d7 <- data.frame(
  LOY = bin(demo$LOY, 7), QUA = bin(demo$QUA, 7),
  SER = bin(demo$SER, 7), ENV = bin(demo$ENV, 7)
)

tt  <- truthTable(d7, outcome = "LOY", conditions = c("QUA", "SER", "ENV"),
                  incl.cut = 0.8, n.cut = 1, show.cases = FALSE)
sol <- minimize(tt)
sol

M1: QUA*SER -> LOY

The solution printed by minimize() matches the row for thrY = 7 in the sweep table from part 1 (QUA*SER). In both cases the data were binarized in the same way (a score of 7 or above is a member), and in both cases the same QCA functions did the minimization.

Record your software versions

Results can depend on the versions of R and the packages. Print them and keep them with your report:

c(R      = R.version.string,
  QCA    = as.character(packageVersion("QCA")),
  ThSQCA = as.character(packageVersion("ThSQCA")))

A short checklist for reporting a sweep

Because a sweep gives you freedom to explore, it also asks you to be transparent. Before you report one, make sure that you can answer yes to each of these questions.

  1. Did I decide the threshold range, and the step between values, before looking at the results?
  2. Do I state every setting that was held fixed (consistency cutoff, frequency cutoff, solution type, directional expectations)?
  3. Did I report every cell of the grid, including the cells with no solution?
  4. Did I check how many cases stand behind the cells at the edges of the grid?
  5. If a cell had several minimal solutions, did I say so?
  6. Did I avoid presenting the sweep as a search for the best threshold?
  7. Did I record the versions of R, QCA, and ThSQCA?

Where to go from here

You now know the whole workflow: choose a sweep, set a defensible range, run it, read it, and report it. If you want more, the package documentation has the full tutorial with additional examples, and the methodology is explained in the paper.

Software used: R 4.6.1, QCA 3.25.5, and ThSQCA 2.0.9.