Bootstrapped Recommendation — brQCA • braQCA

Provides recommendations for consistency score and configurational n thresholds to attain a desired level of confidence in a QCA algorithm application.

Usage

brQCA(
  qca.data,
  outcome = "OUT",
  type = "crisp",
  inclcut = "",
  ncut = 2,
  neg.out = FALSE,
  sim = 10,
  verbose = TRUE
)

Arguments

qca.data: the QCA data frame.
outcome: the outcome variable in the QCA data frame of causal conditions; "OUT" is the outcome variable for an application of QCA.
type: of QCA application, "crisp" or "fuzzy" sets. Default set to type = "crisp".
inclcut: range of consistency scores for inclusion. If not specified, this defaults to seq(from = 0.5, to = 1, by = 0.01).
ncut: configurational n levels to simulate. Can be altered to give options for the range of minimum to maximum ncut value that the truth table yields, by naming the the truth table object (e.g. truth) and calling the minimum and maximum number of cases, using ncut=min(truth$tt$n):max(truth$tt$n) identified by the truth table. Default set to ncut=2.
neg.out: [from QCA package] “Logical, use negation of outcome (ignored if data is a truth table object).” Default set to neg.out=FALSE.
sim: number of simulations to run for each combination of parameters. The final number of simulations is length(inclcut)*length(ncut)*sim*2. Default set to sim=10.
verbose: prints the system time used to run the simulation and the percent complete. Default set to verbose=TRUE.

Value

Significance levels reached (.10,.05, .01, .001) when specifying a combination of inclcut, ncut, and neg.out in a QCA model.

Examples


qca.data <- rallies[,8:13]

if (FALSE) { # \dontrun{
brQCA(qca.data,outcome="P",ncut=5,sim=1)
} # }