theory.evaluation: Performs theory evaluation.

Description

Function that returns membership of cases in the intersections between theory and the empirical solution in the form of a data frame, the names of cases in the intersections between theory and the empirical solution, and the parameters of fit for these intersections.

Usage

theory.evaluation(theory, empirics, outcome, sol = 1, print.fit=FALSE, 
                  print.data=FALSE, consH = FALSE, ...)

Arguments

theory: A character string specifying the theory. Unions of conditions are performed with a "+", while intersections are performed with a "*". Conditions should be capitalized and negated conditions should be inserted with a "~".
empirics: An object of class 'qca'. When performing analyses for the negated outcome, just use the results from the minimize() function for the negation of the outcome.
outcome: A character string with the name of the outcome. When performing analyses of the sufficient solution for the negated outcome one must only use the minimize() result from the sufficiency analysis of the negated outcome in the argument empirics. Changing the name in the argument outcome or using a tilde is not necessary.
sol: A vector where the first number indicates the number of the conservative or parsimonious solution according to the order in the "qca" object. For more complicated structures of model ambiguity, the intermediate solution can also be specified by using a character string of the form "c1p3i2" where c = conservative solution, p = parsimonious solution and i = intermediate solution.
print.data: Logical. Print also the membership of cases in all the intersections between theory and empirics?
print.fit: Logical. Print also the parameters of fit for the intersections between theory and empirics?
consH: Logical. Print also the Haesebrouck's consistency among the parameters of fit?
...: Deprecated arguments (use.tilde).

Author

Ioana-Elena Oana

References

Ragin, C. C. 1987. The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. Berkeley: University of California Press, pp. 118-121

Schneider, C. Q., Wagemann, C. 2012. Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press, chapter 11.3

Examples

Run this code

if (FALSE) {
# Import your data. For example:

data(SCHF)

# Get the intermediate solution:

sol_yi <- minimize(SCHF, outcome = "EXPORT",
                conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
                incl.cut = .9, 
                include = "?", 					   
                details = TRUE, show.cases = TRUE, dir.exp = c(0,0,0,0,0,0))


# Specify the theory. Let's assume the theory says that the 
# absence of EMP and the presence of MA is sufficient for EXPORT:

t<-"~EMP*MA"


# Perform theory evaluation (get only the names of the cases and the Boolean intersections):

TH <- theory.evaluation(theory = t, empirics = sol_yi, outcome = "EXPORT", sol = 1, 
                  print.data=FALSE, print.fit=FALSE)
TH

# Get only the case names:

TH$cases

# Or only the parameters of fit:

TH$fit}