This document provides reproducible code for TSQCA analyses.
It is designed to be inserted directly into a research article’s appendix
or provided as supplementary analysis documentation. All code is fully executable and ordered for reproducibility.

1. Load Packages

library(TSQCA)
library(QCA)
#> Warning: package 'QCA' was built under R version 4.4.3
#> Loading required package: admisc
#> Warning: package 'admisc' was built under R version 4.4.3
#> 
#> To cite package QCA in publications, please use:
#>   Dusa, Adrian (2019) QCA with R. A Comprehensive Resource.
#>   Springer International Publishing.
#> 
#> To run the graphical user interface, use: runGUI()

2. Load Data

# Adjust the file name as needed
library(TSQCA)
data("sample_data")
dat <- sample_data

# Outcome and conditions
outcome  <- "Y"
conditions <- c("X1", "X2", "X3")

# Quick inspection
str(dat)
#> 'data.frame':    80 obs. of  4 variables:
#>  $ Y : int  8 4 5 7 2 2 7 5 3 8 ...
#>  $ X1: int  7 2 6 8 8 9 8 8 1 5 ...
#>  $ X2: int  7 5 8 4 0 5 8 4 4 2 ...
#>  $ X3: int  1 6 6 5 3 4 5 3 6 8 ...
summary(dat)
#>        Y                X1               X2              X3        
#>  Min.   : 0.000   Min.   : 0.000   Min.   : 0.00   Min.   : 0.000  
#>  1st Qu.: 3.000   1st Qu.: 3.000   1st Qu.: 3.00   1st Qu.: 3.000  
#>  Median : 5.000   Median : 5.500   Median : 5.00   Median : 5.000  
#>  Mean   : 5.513   Mean   : 5.237   Mean   : 5.05   Mean   : 4.925  
#>  3rd Qu.: 7.250   3rd Qu.: 8.000   3rd Qu.: 7.25   3rd Qu.: 7.000  
#>  Max.   :10.000   Max.   :10.000   Max.   :10.00   Max.   :10.000

3. Baseline Thresholds

thrY_base <- 7
thrX_base <- 7

# Fixed X thresholds (for OTS–QCA)
thrX_vec <- c(
  X1 = thrX_base,
  X2 = thrX_base,
  X3 = thrX_base
)
thrX_vec
#> X1 X2 X3 
#>  7  7  7

4. CTS–QCA (ctSweepS)

Sweep a single condition X (example: X3).

sweep_var   <- "X3"   # Condition (X) whose threshold is swept
sweep_range <- 6:9    # Candidate threshold values to evaluate
thrY         <- 7     # Outcome (Y) threshold (fixed)
thrX_default <- 7     # Threshold for other X conditions (fixed)

res_cts <- ctSweepS(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_var      = "X3",
  sweep_range    = 6:9,
  thrY           = 7,
  thrX_default   = 7,
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_cts)
#> CTS-QCA Summary
#> =============== 
#> 
#> Analysis Parameters:
#>   Outcome: Y 
#>   Conditions: X1, X2, X3 
#>   Consistency cutoff: 0.8 
#>   Frequency cutoff: 1 
#> 
#> Results by Threshold:
#> 
#>  threshold expression inclS  covS n_solutions
#>          6      X1*X2 1.000 0.303           1
#>          7 X3 + X1*X2 0.906 0.879           1
#>          8 X3 + X1*X2 1.000 0.818           1
#>          9 X3 + X1*X2 1.000 0.515           1

Export:

write.csv(res_cts$summary, file = "TSQCA_CTS_results.csv", row.names = FALSE)

5. MCTS–QCA (ctSweepM)

Sweep multiple X thresholds simultaneously.

# Create a sweep list specifying thresholds for each condition
sweep_list <- list(
  X1 = 6:7,
  X2 = 6:7,
  X3 = 6:7
)

res_mcts <- ctSweepM(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_list     = sweep_list,
  thrY           = 7,
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_mcts)
#> MCTS-QCA Summary
#> ================ 
#> 
#> Analysis Parameters:
#>   Outcome: Y 
#>   Conditions: X1, X2, X3 
#>   Consistency cutoff: 0.8 
#>   Frequency cutoff: 1 
#> 
#> Results by Threshold:
#> 
#>         threshold combo_id     expression inclS  covS n_solutions
#>  X1=6, X2=6, X3=6        1          X1*X2 0.833 0.455           1
#>  X1=7, X2=6, X3=6        2          X1*X2 1.000 0.394           1
#>  X1=6, X2=7, X3=6        3      X1*X2*~X3 0.818 0.273           1
#>  X1=7, X2=7, X3=6        4          X1*X2 1.000 0.303           1
#>  X1=6, X2=6, X3=7        5             X3 0.864 0.576           1
#>  X1=7, X2=6, X3=7        6 ~X1*X3 + X1*X2 0.931 0.818           1
#>  X1=6, X2=7, X3=7        7             X3 0.864 0.576           1
#>  X1=7, X2=7, X3=7        8     X3 + X1*X2 0.906 0.879           1

Export:

write.csv(res_mcts$summary, file = "TSQCA_MCTS_results.csv", row.names = FALSE)

6. OTS–QCA (otSweep)

Sweep only the outcome threshold (Y).

sweep_range_ots <- 6:8

res_ots <- otSweep(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_range    = sweep_range_ots,
  thrX           = thrX_vec,
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_ots)
#> OTS-QCA Summary
#> =============== 
#> 
#> Analysis Parameters:
#>   Outcome: Y 
#>   Conditions: X1, X2, X3 
#>   Consistency cutoff: 0.8 
#>   Frequency cutoff: 1 
#> 
#> Results by Threshold:
#> 
#>  thrY  expression inclS  covS n_solutions
#>     6  X3 + X1*X2 0.906 0.853           1
#>     7  X3 + X1*X2 0.906 0.879           1
#>     8 No solution    NA    NA           0

Export:

write.csv(res_ots$summary, file = "TSQCA_OTS_results.csv", row.names = FALSE)

7. DTS–QCA (dtSweep)

Two-dimensional sweep: X thresholds × Y thresholds.

sweep_list_dts_X <- list(
  X1 = 6:7,
  X2 = 6:7,
  X3 = 6:7
)

sweep_range_dts_Y <- 6:7

res_dts <- dtSweep(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_list_X   = sweep_list_dts_X,
  sweep_range_Y  = sweep_range_dts_Y,
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_dts)
#> DTS-QCA Summary
#> =============== 
#> 
#> Analysis Parameters:
#>   Outcome: Y 
#>   Conditions: X1, X2, X3 
#>   Consistency cutoff: 0.8 
#>   Frequency cutoff: 1 
#> 
#> Results by Threshold:
#> 
#>  thrY combo_id             thrX     expression inclS  covS n_solutions
#>     6        1 X1=6, X2=6, X3=6          X1*X2 0.833 0.441           1
#>     7        1 X1=6, X2=6, X3=6          X1*X2 0.833 0.455           1
#>     6        2 X1=7, X2=6, X3=6          X1*X2 1.000 0.382           1
#>     7        2 X1=7, X2=6, X3=6          X1*X2 1.000 0.394           1
#>     6        3 X1=6, X2=7, X3=6      X1*X2*~X3 0.818 0.265           1
#>     7        3 X1=6, X2=7, X3=6      X1*X2*~X3 0.818 0.273           1
#>     6        4 X1=7, X2=7, X3=6          X1*X2 1.000 0.294           1
#>     7        4 X1=7, X2=7, X3=6          X1*X2 1.000 0.303           1
#>     6        5 X1=6, X2=6, X3=7             X3 0.864 0.559           1
#>     7        5 X1=6, X2=6, X3=7             X3 0.864 0.576           1
#>     6        6 X1=7, X2=6, X3=7 ~X1*X3 + X1*X2 0.931 0.794           1
#>     7        6 X1=7, X2=6, X3=7 ~X1*X3 + X1*X2 0.931 0.818           1
#>     6        7 X1=6, X2=7, X3=7             X3 0.864 0.559           1
#>     7        7 X1=6, X2=7, X3=7             X3 0.864 0.576           1
#>     6        8 X1=7, X2=7, X3=7     X3 + X1*X2 0.906 0.853           1
#>     7        8 X1=7, X2=7, X3=7     X3 + X1*X2 0.906 0.879           1

Export:

write.csv(res_dts$summary, file = "TSQCA_DTS_results.csv", row.names = FALSE)

8. Using extract_mode (New in v0.2.0)

Extract all solutions or essential prime implicants for robustness analysis.

Extract All Solutions

res_all <- otSweep(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_range    = 6:8,
  thrX           = thrX_vec,
  dir.exp        = c(1, 1, 1),
  extract_mode   = "all",
  return_details = TRUE
)

# View results with n_solutions column
head(res_all$summary)

Extract Essential Prime Implicants

res_essential <- otSweep(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_range    = 6:8,
  thrX           = thrX_vec,
  dir.exp        = c(1, 1, 1),
  extract_mode   = "essential",
  return_details = TRUE
)

# View results with essential prime implicants, selective terms, and unique terms
head(res_essential$summary)

9. Generating Reports (New in v0.2.0)

Create comprehensive markdown reports for documentation.

Full Report

generate_report(res_ots, "TSQCA_OTS_report_full.md", dat = dat, format = "full")

Simple Report (for manuscripts)

generate_report(res_ots, "TSQCA_OTS_report_simple.md", dat = dat, format = "simple")

10. Negated Outcome Analysis (New in v0.3.0)

Analyze conditions sufficient for the absence of the outcome.

Standard vs Negated Outcome

# Standard: conditions for Y >= threshold
res_Y <- otSweep(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_range    = 6:8,
  thrX           = thrX_vec,
  dir.exp        = c(1, 1, 1)
)

# Negated: conditions for Y < threshold
res_negY <- otSweep(
  dat            = dat,
  outcome        = "~Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_range    = 6:8,
  thrX           = thrX_vec,
  dir.exp        = c(1, 1, 1)
)

# Compare results
res_Y$summary
res_negY$summary

# Check negation flag
res_negY$params$negate_outcome
# [1] TRUE

11. Accessing Analysis Parameters

All parameters are stored for reproducibility.

# View stored parameters
res_ots$params

# Example output:
# $outcome
# [1] "Y"
# $conditions
# [1] "X1" "X2" "X3"
# $thrX
# X1 X2 X3 
#  7  7  7 
# $incl.cut
# [1] 0.8
# $n.cut
# [1] 1
# $pri.cut
# [1] 0

Configuration Charts (New in v0.5.0)

Configuration charts are now automatically included in reports. You can also generate them separately:

# From path strings
paths <- c("A*B*~C", "A*D")
chart <- config_chart_from_paths(paths)
cat(chart)
#> | Condition | M1 | M2 |
#> |:--:|:--:|:--:|
#> | A | ● | ● |
#> | B | ● |  |
#> | C | ⊗ |  |
#> | D |  | ● |
#> 
#> *● = presence, ⊗ = absence, blank = don't care*

For reports:

# Charts are included by default
generate_report(result, "report.md", dat = dat, format = "full")

# Use LaTeX symbols for academic papers
generate_report(result, "report.md", dat = dat, chart_symbol_set = "latex")

References

For more information on TS-QCA methodology, see:

Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press. DOI: 10.7208/chicago/9780226702797.001.0001
Duşa, A. (2019). QCA with R: A Comprehensive Resource. Springer. DOI: 10.1007/978-3-319-75668-4
Oana, I.-E., & Schneider, C. Q. (2024). A Robustness Test Protocol for Applied QCA: Theory and R Software Application. Sociological Methods & Research, 53(1), 57–88. DOI: 10.1177/00491241211036158

12. Session Information

sessionInfo()
#> R version 4.4.2 (2024-10-31 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26200)
#> 
#> Matrix products: default
#> 
#> 
#> locale:
#> [1] LC_COLLATE=C                    LC_CTYPE=Japanese_Japan.utf8   
#> [3] LC_MONETARY=Japanese_Japan.utf8 LC_NUMERIC=C                   
#> [5] LC_TIME=Japanese_Japan.utf8    
#> 
#> time zone: Asia/Tokyo
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] QCA_3.23    admisc_0.39 TSQCA_1.0.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] cli_3.6.5         knitr_1.50        rlang_1.1.6       xfun_0.55        
#>  [5] otel_0.2.0        promises_1.5.0    shiny_1.12.1      jsonlite_2.0.0   
#>  [9] xtable_1.8-4      htmltools_0.5.9   httpuv_1.6.16     sass_0.4.10      
#> [13] lpSolve_5.6.23    rmarkdown_2.30    evaluate_1.0.4    jquerylib_0.1.4  
#> [17] fastmap_1.2.0     yaml_2.3.10       lifecycle_1.0.4   compiler_4.4.2   
#> [21] Rcpp_1.1.0        rstudioapi_0.17.1 later_1.4.4       digest_0.6.39    
#> [25] R6_2.6.1          magrittr_2.0.4    bslib_0.9.0       declared_0.25    
#> [29] tools_4.4.2       mime_0.13         venn_1.12         cachem_1.1.0

TSQCA Reproducible Code (English)

1. Load Packages

2. Load Data

3. Baseline Thresholds

4. CTS–QCA (ctSweepS)

5. MCTS–QCA (ctSweepM)

6. OTS–QCA (otSweep)

7. DTS–QCA (dtSweep)

8. Using extract_mode (New in v0.2.0)

Extract All Solutions

Extract Essential Prime Implicants

9. Generating Reports (New in v0.2.0)

Full Report

Simple Report (for manuscripts)

10. Negated Outcome Analysis (New in v0.3.0)

Standard vs Negated Outcome

11. Accessing Analysis Parameters

Configuration Charts (New in v0.5.0)

References

12. Session Information