SetMethods (version 2.1)

QCAfit: Function calculating the parameters of fit

Description

QCAfit is a function calculating parameters of fit useful in QCA and fsQCA that are consistency, coverage, PRI, Haesebrouck's consistency, RoN and PRODUCT. It works with both single and multiple conditions.

Usage

QCAfit(x, y, cond.lab = NULL, necessity = FALSE, neg.out = FALSE, product = FALSE)

Arguments

x

is a vector containing the values of a condition or a matrix with more than one conditions.

y

is a vector containing the values of the output.

cond.lab

is a vector containing the label(s) of the condition(s).

necessity

logical. It indicates if the output should be for sufficient or necessary condition(s). By default, FALSE, the function returns a table of parameters of fit for sufficient condition(s) (Consistency, Coverage, PRI, Haesebrouck's Consistency, and optionally Product). When it set to TRUE the function returns a table of parameters of fit for necessary condition(s) (Consistency, Coverage, Relevance of Necessity).

neg.out

logical. It indicates if the parameters of fit should be computed for the positive or the negative outcome. By default, FALSE, the function returns parameters of fit for the positive outcome.

product

logical. It indicates whether the parameter of fit PRODUCT should be shown. This stands for the product between the consistency sufficiency parameter and the PRI parameter.

Value

It returns a matrix containing the parameters of fit for each condition.

References

Haesebrouck, T. (2015) Pitfalls in QCA's consistency measure. Journal of Comparative Politics 2:65-80.

Ragin, C. C. 2006. Set Relations in Social Research: Evaluating Their Consistency and Coverage. Political Analysis 14(3): 291-310.

Schneider, C. Q., Wagemann, C. (2012) Set-Theoretic Methods for the Social Sciences, Cambridge University Press: Cambridge.

Schneider, C. Q., Wagemann, C., Quaranta, M. (2012) How To... Use Software for Set-Theoretic Analysis. Online Appendix to "Set-Theoretic Methods for the Social Sciences". Available at www.cambridge.org/schneider-wagemann

Examples

# NOT RUN {
# Generate fake data
set.seed(1234)

a <- runif(100, 0, 1)
b <- runif(100, 0, 1)
c <- runif(100, 0, 1)
y <- runif(100, 0, 1)

# Only one condition, for sufficiency
QCAfit(a, y, cond.lab = "A")

# With three conditions, for sufficiency
QCAfit(cbind(a, b, c), y, cond.lab = c("A", "B", "C"))

# Only one condition, for necessity
QCAfit(a, y, cond.lab = "A", necessity = TRUE)

# With three conditions, for necessity
QCAfit(cbind(a, b, c), y, cond.lab = c("A", "B", "C"), necessity = TRUE)

# With three conditions and negated output, for sufficiency
QCAfit(cbind(a, b, c), y, cond.lab = c("A", "B", "C"), neg.out = TRUE)
# }