I had to guess without a reprex. See the FAQ. Does the help() example work on your installation and is comparable to your data structure and call?
library(SetMethods)
#> Loading required package: QCA
#> Loading required package: admisc
#>
#> To cite package QCA in publications, please use:
#> Dusa, Adrian (2019) QCA with R. A Comprehensive Resource.
#> Springer International Publishing.
#>
#> To run the graphical user interface, use: runGUI()
#> Loading required package: ggplot2
#> Loading required package: ggrepel
#> Loading required package: stargazer
#>
#> Please cite as:
#> Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
#> R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
#>
#>
#> To cite package SetMethods in publications use:
#>
#> Oana, Ioana-Elena and Carsten Q. Schneider. 2018. SetMethods: An Add-on R Package for
#> Advanced QCA. The R Journal 10(1): 507-33. https://journal.r-project.org/archive/2018/RJ-2018-031/index.html
#>
#>
# 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 necessity
QCAfit(a, y, cond.lab = "A")
#> Cons.Nec Cov.Nec RoN
#> A 0.593 0.705 0.813
# With three conditions and their negation, for necessity
QCAfit(cbind(a, b, c), y)
#> Cons.Nec Cov.Nec RoN
#> a 0.593 0.705 0.813
#> b 0.694 0.682 0.737
#> c 0.653 0.677 0.755
#> ~a 0.721 0.666 0.700
#> ~b 0.611 0.675 0.775
#> ~c 0.674 0.702 0.771
# Only one condition, for sufficiency
QCAfit(a, y, cond.lab = "A", necessity = FALSE)
#> Cons.Suf Cov.Suf PRI
#> 0.705 0.593 0.480
# With three conditions, their negation and negated output, for necessity
QCAfit(cbind(a, b, c), y, neg.out = TRUE)
#> Cons.Nec Cov.Nec RoN
#> a 0.609 0.668 0.795
#> b 0.681 0.618 0.699
#> c 0.691 0.661 0.746
#> ~a 0.731 0.624 0.674
#> ~b 0.650 0.662 0.769
#> ~c 0.663 0.638 0.735
# Load the Schneider data:
data(SCHF)
# Get parameters of fit for condition EMP as necessary for outcome EXPORT:
QCAfit(SCHF$EMP, SCHF$EXPORT, cond.lab = "EMP")
#> Cons.Nec Cov.Nec RoN
#> EMP 0.58 0.607 0.698
QCAfit(1-SCHF$EMP, SCHF$EXPORT, neg.out=TRUE, cond.lab = "~EMP")
#> Cons.Nec Cov.Nec RoN
#> ~EMP 0.545 0.518 0.695
# Obtain the parsimonious solution for outcome "EXPORT":
sol_yp <- minimize(SCHF, outcome = "EXPORT",
conditions = c("EMP","BARGAIN","UNI","OCCUP","STOCK", "MA"),
incl.cut = .9,
include = "?",
details = TRUE, show.cases = TRUE)
# Get parameters of fit for the parsimonious solution:
QCAfit(x = sol_yp, y = "EXPORT", necessity = FALSE)
#> Cons.Suf Cov.Suf PRI
#> ~EMP*OCCUP 0.836 0.353 0.596
#> BARGAIN*UNI*STOCK 0.796 0.497 0.665
#> ~OCCUP*STOCK*~MA 0.890 0.374 0.788
#> solution_formula 0.790 0.742 0.675
# Get parameters of fit for truth table rows 2,8, and 10:
QCAfit(x = sol_yp, y = "EXPORT", ttrows=c("2","8","10"), necessity = FALSE)
#> Cons.Suf Cov.Suf PRI
#> 2 0.7869 0.0461 0.3158
#> 8 0.9303 0.0899 0.7760
#> 10 0.6615 0.0723 0.1585
# Get parameters of fit for truth table rows 2,8, and 10:
QCAfit(x = sol_yp, y = "EXPORT", ttrows=c("2","8","10"), necessity = FALSE)
#> Cons.Suf Cov.Suf PRI
#> 2 0.7869 0.0461 0.3158
#> 8 0.9303 0.0899 0.7760
#> 10 0.6615 0.0723 0.1585
Created on 2023-02-05 with reprex v2.0.2