TSQCA (version 1.0.0)

dtSweep: DTS–QCA: Two-dimensional X–Y threshold sweep

Description

Sweeps thresholds for multiple X variables and the outcome Y jointly. For each combination of X thresholds and each candidate Y threshold, the data are binarized and a crisp-set QCA is executed.

Usage

dtSweep(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_list_X,
  sweep_range_Y,
  dir.exp = NULL,
  include = "?",
  incl.cut = 0.8,
  n.cut = 1,
  pri.cut = 0,
  extract_mode = c("first", "all", "essential"),
  return_details = TRUE,
  Yvar = NULL,
  Xvars = NULL
)

Value

If return_details = FALSE, a data frame with columns:

• combo_id — index of threshold combination

• thrY — threshold for Y

• thrX — character summary of X thresholds

• expression — minimized solution expression

• inclS — solution consistency

• covS — solution coverage

• (additional columns depending on extract_mode)

If return_details = TRUE, a list with:

• summary — the data frame above

• details — list of runs with combo_id, thrY, thrX_vec, truth_table, solution

Arguments

dat

Data frame containing the outcome and condition variables.

outcome

Character. Outcome variable name. Supports negation with tilde prefix (e.g., "~Y") following QCA package conventions.

conditions

Character vector. Names of condition variables.

sweep_list_X

Named list. Each element is a numeric vector of candidate thresholds for the corresponding X.

sweep_range_Y

Numeric vector. Candidate thresholds for Y.

dir.exp

Directional expectations for minimize. If NULL, all set to 1.

include

Inclusion rule for minimize.

incl.cut

Consistency cutoff for truthTable.

n.cut

Frequency cutoff for truthTable.

pri.cut

PRI cutoff for minimize.

extract_mode

Character. How to handle multiple solutions: "first" (default), "all", or "essential". See qca_extract for details.

return_details

Logical. If TRUE (default), returns both summary and detailed objects for use with generate_report().

Yvar

Deprecated. Use outcome instead.

Xvars

Deprecated. Use conditions instead.

Examples

# Load sample data
data(sample_data)

# Quick demonstration with reduced complexity (< 5 seconds)
# Using 2 conditions and 2 threshold levels
sweep_list_X <- list(
  X1 = 6:7,  # Reduced from 6:8 to 6:7
  X2 = 6:7   # Reduced from 6:8 to 6:7
)

sweep_range_Y <- 6:7  # Reduced from 6:8 to 6:7

# Run dual threshold sweep with reduced parameters
# This explores 2 × 2^2 = 8 threshold combinations
result_quick <- dtSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),  # Reduced from 3 to 2 conditions
  sweep_list_X = sweep_list_X,
  sweep_range_Y = sweep_range_Y
)
head(result_quick$summary)

# \donttest{
# Full analysis with all conditions and thresholds
# This explores 3 × 3^3 = 81 threshold combinations (takes ~10-15 seconds)
sweep_list_X_full <- list(
  X1 = 6:8,
  X2 = 6:8,
  X3 = 6:8
)

sweep_range_Y_full <- 6:8

result_full <- dtSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_list_X = sweep_list_X_full,
  sweep_range_Y = sweep_range_Y_full
)

# Analyze threshold-dependent causal structures
head(result_full$summary)
# }