The Analysis of Contingency Tables with Incompletely Classified Data

Abstract

In many practical situations, investigators are forced to study the structure underlying the cross-classification of several categorical variables via tables of observed counts in which the observations corresponding to certain sets of cells are indistinguishable. Methods are presented for the analysis of such contingency tables with incompletely cross-classified data via loglinear models. The method of maximum likelihood is used to estimate the expected cell counts which are then used to test the goodness-of-fit of the model. Extensions to incomplete (or truncated) contingency tables are indicated and several examples are given. [Prevalence of single and mixed malarial infections (Plasmodium falciparum, P. vivax, P. malariae) of children in Trinidad and Tobago is discussed. The amino acid composition of human protein molecules is discussed with regard to evolution.].