qcapower: 'qcapower' returns a power estimate with regard to the...

In qcapower: Estimate Power and Required Sample Size in QCA

Description

qcapower allows you to estimate power for a term. Probability is the probability of rejecting the null hypothesis that no set relation is in plaace when it is in place, in fact. A term can be a single condition, a conjunction, or a disjunction of any combination of the two.

Usage

qcapower(
  cases,
  null_hypo,
  alt_hypo,
  sims = 1000,
  perms = 10000,
  alpha = 0.05,
  cons_threshold = 0.01,
  set_seed = 135
)

Arguments

cases	Number of cases. In fuzzy-set QCA, equal to total number of cases in the analysis
null_hypo	Null hypothesis (H0). Consistency value separating consistent from inconsistent terms. It is the highest possible consistency value that would let you conclude that no set relation is given.
alt_hypo	Alternative hypothesis (H1). Expected, actual consistency value of term.
sims	Number of simulations for calculating power
perms	Number of permutations of hypothetical dataset per simulation run
alpha	Level of alpha at which statistical significance of H0 is tested
cons_threshold	Degree of tolerance in generating hypothetical data with consistency equaling alt_hypo (see vignette)
set_seed	Parameter for achieving reproducibility of estimate

Value

A dataframe with rows equaling the number of sims. power is the power estimate and is identical for each rows. powercum is the running power estimate up to this row. quant is the 5%-quantile of the permuted distributions. See the vignette for more information.

See Also

qp_quant_plot and qp_run_plot

Examples

power_data <- qcapower(cases = 20, null_hypo = 0.8, alt_hypo = 0.95, sims = 10, perms = 1000)
head(power_data)

qcapower documentation built on March 2, 2020, 5:09 p.m.