brQCA {braQCA}

Bootstrapped Recommendation

Description

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]

## Not run: 
brQCA(qca.data,outcome="P",ncut=5,sim=1)

## End(Not run)