conversion function

Conversion between Representations of Responses or States

Converts between binary matrix and pattern representations of response patterns or knowledge states.

as.pattern(R, freq = FALSE, useNames = FALSE, as.set = FALSE,
           sep = "", emptyset = "{}", as.letters = NULL)

as.binmat(N.R, uniq = TRUE, col.names = NULL, as.logical = FALSE)

is.subset(R)

Arguments

• R: an indicator matrix of response patterns or knowledge states.

• N.R: either a (named) vector of absolute frequencies of response patterns; or a character vector of response patterns or knowledge states; or a set of sets representing the knowledge structure.

• freq: logical, should the frequencies of response patterns be reported?

• uniq: logical, if TRUE, only the unique response patterns are returned.

• useNames: logical, return response patterns as combinations of item names.

• as.set: logical, return response patterns as set of sets.

• sep: character to separate the item names.

• emptyset: string representing the empty set if useNames

  is TRUE.

• as.letters: deprecated, use useNames instead.

• col.names: column names for the state or response matrix.

• as.logical: logical, return logical matrix of states.

Returns

as.pattern returns a vector of integers named by the response patterns if freq is TRUE, else a character vector. If as.set is TRUE, the return value is of class set.

as.binmat returns an indicator matrix. If as.logical is TRUE, it returns a logical matrix.

is.subset returns a logical incidence matrix of the subset relation among states.

See Also

blim, set in package sets.

Examples

data(DoignonFalmagne7)
K <- DoignonFalmagne7$K
as.pattern(K, freq = TRUE)
as.pattern(K)
as.pattern(K, useNames = TRUE)
as.pattern(K, as.set = TRUE)

N.R <- DoignonFalmagne7$N.R
dim(as.binmat(N.R))
dim(as.binmat(N.R, uniq = FALSE))

## Knowledge structure as binary matrix
as.binmat(c("000", "100", "101", "111"))
as.binmat(set(set(), set("a"), set("a", "c"), set("a", "b", "c")))
as.binmat(c("000", "100", "101", "111"), as.logical = TRUE)

## Subset relation incidence matrix
is.subset(K)

## Plotting the knowledge structure
if(requireNamespace("relations") &&
   requireNamespace("Rgraphviz")) {
  rownames(K) <- as.pattern(K, useNames = TRUE)
  plot(relations::as.relation(is.subset(K)), main = "")
}