linearities: This function calculates the linearities in the negative...

In divoRce: Diagnosing Separation Phenomena in Categorical Response Models

This function calculates the linearities in the negative structure vector matrix X*, so the row vectors for which there is no separation. If this is an empty set or of length 0, then we have overlap.

Description

The function uses either a response vector y and a design matrix X, or a structure vector matrix S. If S is given, y and X and model are ignored.

Usage

linearities(
  y,
  X,
  S,
  rational = FALSE,
  model = c("b", "bcl", "acl", "cl", "sl", "osm")
)

Arguments

y	the outcome variable. Can be factor, ordered, numeric, character or boolean. Works best if it is a factor or ordered factor. If it is not an (ordered) factor, we treat the outcome as nominal.
X	a design matrix, e.g. generated via a call to 'model.matrix'. This means we expect that X already contains the desired contrasts for factors (e.g., dummies) and any other expanded columns (e.g., for polynomials).
S	a matrix of structure vectors
rational	boolean flag whether rational arithmetic should be used. Default is FALSE.
model	what model class is intended to be fitted? Can be any of "b" for binary, "bcl" for baseline-category link, "cl" for cumulative link, "acl" for adjacent-category link. "sl" for sequential link, "osm" for ordered stereotype model. If missing or NULL it defaults to cumulative link for ordinal y and baseline-category for everything else.

Value

a list with elements $lins which lists the row vectors that are linearities for any category and $index which gives the row index of the linearities. If there is complete separation they are both empty set. If their length/row dimension is number of categories*dim(X)[1], there is overlap. Anything in between is quasi-complete separation.

divoRce documentation built on April 28, 2026, 3:01 a.m.