The data should always be provided as a data frame, with calibrated columns.Calibration can be either crisp, with 2 or more values starting from 0, or fuzzy with
continous scores from 0 to 1. Raw data containing relative frequencies can
also be continous between 0 and 1, but these are not calibrated, fuzzy data.
Some columns can contain the placeholder "-" indicating a “don't care”,
which is used to indicate the temporal order between other columns in tQCA. These
special columns are not causal conditions, hence no parameters of fit will be
calculated for them.
The argument outcome specifies the column name to be explained.
If the outcome is a multivalue column, it can be specified in curly bracket notation,
indicating the value to be explained (the others being automatically converted to
zero).
The outcome can be negated using a tilde operator ~X. The logical argument
neg.out is now deprecated, but still backwards compatible. Replaced by the tilde
in front of the outcome name, it controls whether outcome is to be explained or
its negation.
If the outcome column is multi-value, the argument outcome should use the standard
curly-bracket notation X{value}. Multiple values are allowed, separated by a comma
(for example X{1,2}). Negation of the outcome can also be performed using the tilde
~ operator, for example ~X{1,2}, which is interpreted as:
"all values in X except 1 and 2" and it becomes the new outcome to be explained.
Using both neg.out = TRUE and a tilde ~ in the outcome name don't cancel
each other out, either one (or even both) signaling if the outcome should be
negated.
The argument conditions specifies the causal conditions' names among the other
columns in the data. When this argument is not specified, all other columns except for
the outcome are taken as causal conditions.
A good practice advice is to specify both outcome and conditions
as upper case letters. It is possible, in a next version, to negate outcomes using lower case
letters, situation in which it really does matter how the outcome and/or conditions are
specified.
The argument n.cut specifies the frequency threshold under which a truth
table row is coded as a remainder, irrespective of its inclusion score.
The argument incl.cut replaces the (deprecated, but still backwards compatible)
former arguments incl.cut1 and incl.cut0. Most of the analyses use
the inclusion cutoff for the presence of the output (code "1"). When users need
both inclusion cutoffs (see below), incl.cut can be specified as a vector of
length 2, in the form: c(ic1, ic0) where:
ic1 |
| is the inclusion cutoff for the presence of the output, |
|
|
a minimum sufficiency inclusion score above which the output
value is coded with "1". |
| |
ic0 |
| is the inclusion cutoff for the absence of the output, |
If not specifically declared, the argument ic0 is automatically set equal to
ic1, but otherwise ic0 should always be lower than ic1.
Using these two cutoffs, the observed combinations are coded with:
"1" |
if they have an inclusion score above ic1 |
| |
"C" |
if they have an inclusion score below ic1 and above ic0 (contradiction) |
| |
When argument show.cases is set to TRUE, the case names will be
printed at their corresponding row in the truth table. The resulting object always contains
the cases for each causal combination, even if not printed on the screen (the print function
can later be used to print them).
The sort.by argument orders all configurations by any of the columns
present in the truth table. Typically, sorting occurs by their outcome value,
and/or by their inclusion score, and/or by their frequency, in any order.
Sorting decreasingly (the default) or increasingly can be specified adding the signs
- or +, next after the column name in argument
sort.by (see examples). Note that - is redundant, because
it is the default anyways.
The order specified in this vector is the order in which the configurations will
be sorted. When sorting based on the OUTput column, remainders will always be sorted last.
The argument use.letters controls using the original names of the causal
conditions, or replace them by single letters in alphabetical order. If the
causal conditions are already named with single letters, the original letters
will be used.
The argument inf.test combines the inclusion score with a statistical
inference test, in order to assign values in the output column OUT. For the moment, it
is only the binomial test, which needs crisp data (it doesn't work with fuzzy sets).
Following a similar logic as above, for a given (specified) critical significance level,
the output for a truth table row will be coded as:
"1" |
if the true inclusion score is significanly higher than ic1, |
| |
"C" |
contradiction, if the true inclusion score is not significantly higher than ic1 |
|
|
but significantly higher than ic0, |
| |
It should be noted that statistical tests perform well only when the number of cases is large,
otherwise they are usually not significant. For a low number of cases, depending on the inclusion
cutoff value(s), it will be harder to code a value of "1" in the output, and also
harder to obtain contradictions if the true inclusion is not signficantly higher than ic0.
The argument complete controls how to print the table on the screen, either
complete (when set to TRUE), or just the observed combinations (default).
For up to 7 causal conditions, the resulting object will always contain the complete
truth table, even if it's not printed on the screen. This is useful for multiple reasons:
researchers like to manually change output values in the truth table (sometimes including
in this way a remainder, for example), and it is also useful to plot Venn diagrams, each
truth table row having a correspondent intersection in the diagram.