fs_truthTable {QCA3} | R Documentation |
Constructing a truthTable from fuzzy set score.
fs_truthTable(mydata, outcome, conditions, ncases_cutoff = 1, consistency_cutoff = 0.8, complete = FALSE, show.cases =TRUE,quiet =FALSE, cases = NULL, ...)
mydata |
A fuzzy set score dataset. All the scores must range from 0 to 1. |
outcome |
character, the name of the outcome variable in the dataset. |
conditions |
character vetor, the name of the conditions from the dataset. |
ncases_cutoff |
When number of case is less than cutoff, it will be regarded as dontcare configuration. |
consistency_cutoff |
Cutoff point of consistenty score, cases with consistency score greater than cutoff point are regarded as OUT=1. |
complete |
prints the complete truth table, including configurations without empirical cases. |
show.cases |
show the rownames from the original dataset for each combination of conditions. |
quiet |
Not used currently. |
cases |
character, variable of case names. If it is NULL and show.cases is TRUE, name of cases are derived from row names of dataset. |
... |
Not used currently. |
There are serveral pillars which make it possible to construct a crip
truthTable summarizing the raw data. There is a correspondance between
vector space corners and truthTable rows. Thus, it is possible to get
the number of cases with strong membership in each corner (usually
greater then 0.5), and the consistency of the empirical evidence for
each corner. By specifying the frequency thresholds for fuzzy-set
assessments (the ncases_cutoff
argument), and assessing the
consistency of fuzzy-set subset relations (the consistency_cutoff
argument), we can finally construct a truthTable.
A truthTable.
Ronggui HUANG
Ragin. Charles. 2009. Qualitative Comparative Analyais Using Fuzzy Sets (fsQCA). In Configuraional comparative Methods: qualitative comparative analysis (QCA) and related techniques. ed by Benoit RiHoux and Charles Ragin. Sage. This chapter can be downloaded from http://www.u.arizona.edu/~cragin/fsQCA/software.shtml.
fs_truthTable(Lipset_fs,"Survived.FZ",c("Developed.FZ","Urban.FZ","Literate.FZ","Industrial.FZ", "Stable.FZ"),cases="Country",consistency_cutoff=0.7)