Description Usage Arguments Details Value Author(s) References See Also Examples

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

This function can be used for crip set TQCA as well. See
`CarenPanofsky`

for example. It needs manual construction
of indicator conditions of temporal order.

1 2 3 4 5 | ```
cs_truthTable(mydata, outcome, conditions,
method = c("deterministic", "probabilistic", "mixed"),
weight = NULL, complete=FALSE, show.cases = TRUE, cases = NULL,
cutoff1 = 1, cutoff0 = 1, benchmark = 0.65, conf.level = 0.95,
missing = c("missing", "dontcare", "positive", "negative"))
``` |

`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. |

`weight` |
character, name of a variable specifying the weights. |

`complete` |
logical, when it is TRUE the result includes configurations without empirical cases. |

`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. |

`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. |

`missing` |
method to handle missing data. |

The value of all the conditions should start from 0. In cript set QCA, it is always be 0 or 1. Value -9 in conditions means "don't care" (though "don't care" in outcome is denoted by "-9").

Symbols of the outcome. '1' is postive configuration, '0' is negative configuration, 'C' is contraditory configuration, "?" is unobserved configuration and '-9' 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 or cutoff0 multiplied by the total number of case.

'benchmark' and 'conf.level' are only meaningful for 'probabilistic' and "mixed" 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.

For method of 'mixed', a statistical test will conducted only for contradictory configurations 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.

rownames of a truthTable is grouping index (not important for end-users).

There is a sort method method for the truthTable object.

An object of class "cs_trutbTable" and "truthTable". A list with 5 components:

`truthTable` |
a data frame presenting a truthTable. |

`outcome` |
The name of outcome variable. length-1 character. |

`conditions` |
conditions. A character vector. |

`nlevels` |
integer vector specifying number of levels of each condition. |

`call` |
the matched call. |

Ronggui HUANG

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

`fs_truthTable`

, `mv_truthTable`

and `reduce`

1 2 3 4 5 6 7 8 9 | ```
## truthTable for csQCA
cs_truthTable(Lipset_cs,"SURVIVAL",
c("GNPCAP", "URBANIZA", "LITERACY", "INDLAB", "GOVSTAB"),case="CASEID")
cst <- cs_truthTable(Lipset_cs,"SURVIVAL",
c("GNPCAP", "URBANIZA", "LITERACY", "INDLAB", "GOVSTAB"),
case="CASEID",complete=TRUE)
sort(cst)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.