Graphics for Regressions with a Binary Response

Abstract

Abstract Central dimension-reduction subspaces, which characterize the dependence of a response variable on one or more predictors, are developed and then used to guide the construction and interpretation of graphics for regression problems with a binary response variable. Graphical methods requiring neither a link function nor residuals are suggested for both development and criticism of model components implied by the central dimension-reduction subspace. Key Words: Dimension-reduction subspacesGraphical regressionLogistic regressionSliced inverse regression