cs_truthTable {QCA3}R Documentation

Construct a truthTable for csQCA or mvQCA

Description

Construct a truthTable for csQCA and mvQCA. Both deterministic and probabilistic methods of determining configurations of positive, negative and contraditory outcome are implemented.

Usage

cs_truthTable(mydata, outcome, conditions,
              method = c("deterministic","probabilistic"),
              complete = FALSE, weight = NULL, show.cases = TRUE,
              cases = NULL, nlevels = rep(2, length(conditions)),
              cutoff1 = 1, cutoff0 = 1, benchmark = 0.65, conf.level = 0.95)

Arguments

mydata data frame of the raw data.
outcome character, the name of the outcome variable in mydata.
conditions character vector, the name of the conditions from mydata.
method character, specifying the method of determining the outcome of a configuration.
complete logical, when it is TRUE the result includes configurations without empirical cases.
weight character, name of a variable specifying the weights.
show.cases logical, when TRUE the result shows case names.
cases character, variable specifying the case names. When it is NUll, then use row names of mydata as case names.
nlevels a integer vector, specifying the number of levels of each conditions.
cutoff1 length one numeric vector.
cutoff0 length one numeric vector.
benchmark Benchmark for statistical test. Must equal or greater than 0.5.
conf.level confident level of statistical test.

Details

Symbols of the outcome. '1' is postive configuration, '0' is negative configuration, 'C' is contraditory configuration, and '-' is don't care configuration. When show.case is TRUE and a configuration is 'C', then the name of case with negative outcome is highlighted by [].

'cutoff1' and 'cutoff0' are only meaningful for'deterministic' method. They represent cutting point of positive case and negative case. When a configuration has positive case only and the number of cases is equal or greater than the cutting point, then it is regared as positive outcome, otherwise as don't care outcome. Similarly, When a configuration has negative case only and the number of cases is equal or greater than the cutting point, then it is regared as negative outcome, otherwise as don't care outcome. If a configuration has both positive case and nagetive case, the number of each type of cases will be compared with the corresponding cutting point. If only one type of case have enough case (number of cases greater than cutting point), that configuration is regarded as that type. If both types have enough case, it is contraditory configuration. If neither type has enough case, it is don't care configuration.

The caculation of cutting point: if it is equal or greater than 1, the cutting point is the value of cutoff1 and cutoff0. If it is between 0 and 1, then the cutting point is the cutoff1/cutoff0 multiplied by the total number of case.

'benchmark' and 'conf.level' are only meaningful for 'probabilistic' method. When the method is 'probabilistic', a statistical test will conducted to test if the proportion of case for a configuration is greater then a benchmark. If the proportion of cases with outcome '1' is greater than benchmark, then the it is a configuratin with outcome '1'. If the proportion of case with outcome '0' is greater than benchmark, then the configuration with outcome of '0'. If neither proportion fits the criterion, then it is don't care configuration. There is no contraditory congfiguration in this method, as it is designed to handle with contraditory configurations.

Value

A truthTable.

Author(s)

Ronggui HUANG

References

Ragin, Charles. 2000. Fuzzy-Set Social Science. Pp109-116. University Of Chicago Press.

See Also

fs_truthTable reduce

Examples

## truthTable for csQCA
cs_truthTable(Lipset_cs,"SURVIVAL", c("GNPCAP", "URBANIZA", "LITERACY",
  "INDLAB", "GOVSTAB"),case="CASEID")

## truthTable for mvQCA, please note the nlevels argument.
cs_truthTable(Lipset_mv,"SURVIVAL", c("GNPCAP", "URBANIZA", "LITERACY",
  "INDLAB"),case="CASEID",nlevels=c(3,2,2,2))

[Package QCA3 version 0.0-2 Index]