TSQCA (version 1.3.1)

otSweep: OTS–QCA: Outcome threshold sweep

Description

Sweeps the threshold of the outcome Y while keeping the thresholds of all X conditions fixed.

Usage

otSweep(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_range,
  thrX,
  pre_calibrated = NULL,
  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:

• thrY — threshold for Y

• 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 — per-Y-threshold list of 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_range

Numeric vector. Candidate thresholds for Y.

thrX

Named numeric vector. Fixed thresholds for X variables. Names must match the conditions that require binarization. Variables listed in pre_calibrated do not need a thrX entry.

pre_calibrated

Character vector or NULL. Names of condition variables that have been pre-calibrated (e.g., via QCA::calibrate()) and should be passed through to QCA::truthTable() without binarization. These variables must contain values in the [0, 1] range. Variables not listed here will be binarized using thrX thresholds as usual. Default is NULL (all variables binarized). It is recommended to sweep variables on their original (raw) scale rather than as pre-calibrated fuzzy values, because raw-scale thresholds are easier to interpret substantively.

dir.exp

Directional expectations for minimize. If NULL (default), no directional expectations are applied. To compute the intermediate solution, specify a numeric vector (1, 0, or -1 for each condition). Example: dir.exp = c(1, 1, 1) for three conditions all expected to contribute positively.

include

Inclusion rule for minimize. "" (default, QCA compatible) computes the complex solution without logical remainders. Use "?" to include logical remainders for parsimonious (with dir.exp = NULL) or intermediate solutions (with dir.exp specified).

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)

# Set fixed thresholds for conditions
thrX <- c(X1 = 7, X2 = 7, X3 = 7)

# === Three Types of QCA Solutions ===

# 1. Complex Solution (default, QCA compatible)
#    Does not use logical remainders (most conservative)
result_comp <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 7,
  thrX = thrX
  # include = "" (default), dir.exp = NULL (default)
)
head(result_comp$summary)

# 2. Parsimonious Solution (include = "?")
#    Uses logical remainders without directional expectations
result_pars <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 7,
  thrX = thrX,
  include = "?"  # Include logical remainders
)
head(result_pars$summary)

# 3. Intermediate Solution (include = "?" + dir.exp)
#    Uses logical remainders with directional expectations
result_int <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 7,
  thrX = thrX,
  include = "?",
  dir.exp = c(1, 1, 1)  # All conditions expected positive
)
head(result_int$summary)

# === Threshold Sweep Example ===

# Sweep with complex solutions (default)
result_sweep <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = thrX
)
head(result_sweep$summary)

# Run with negated outcome (~Y)
# Analyzes conditions for Y < threshold
result_neg <- otSweep(
  dat = sample_data,
  outcome = "~Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = thrX
)
head(result_neg$summary)