Boolean function minimization

Which combinations of binary conditions produce the outcome? TDA's bfa builds the truth table of y over the condition variables, finds all prime implicants (Quine-McCluskey), and selects minimal covers. This is the machinery behind crisp-set QCA-style analyses.

Usage

tda_boolean_min(
  y,
  conditions,
  data = NULL,
  undefined = c("dont_care", "true", "false"),
  algorithm = c("lawler", "petrick", "lawler2", "none"),
  max_implicants = NULL,
  options = list(),
  dir = tempfile("tda"),
  ...
)

Arguments

y

the outcome: a 0/1 vector, or a column name in data.

conditions

the condition variables: a 0/1 matrix or data frame, or column names in data (at most 15).

data

optional data frame supplying the columns.

undefined

how rows never observed are treated: "dont_care" (default), "true", or "false".

algorithm

cover selection: "lawler" (default), "petrick", "lawler2", or "none" for prime implicants only.

max_implicants

storage cap.

options

a named list of further TDA options for bfa (ptab, prot, ...).

dir

working directory.

...

passed to tda_run.

Value

A list: $selections, a data frame with one row per term of each minimal cover – selection number and one column per condition holding 1, 0, or NA for "does not matter" – and $expression, the printed symbolic forms.

See also

Other optimization: tda_mlp(), tda_mlpi(), tda_mls(), tda_mqap(), tda_mqp()

Examples

d <- expand.grid(X1 = 0:1, X2 = 0:1, X3 = 0:1)
d$Y <- as.integer(d$X1 & d$X2 | d$X3)
tda_boolean_min("Y", c("X1", "X2", "X3"), data = d)$selections
#>   selection X1 X2 X3
#> 1         1  1  1 NA
#> 2         1 NA NA  1