A simultaneous representation of individuals and variables in a data matrix is called a biplot. When variables are binary, nominal, or ordinal, a classical linear biplot representation is not adequate. Recently, biplots for categorical data-based logistic response models have been proposed. The coordinates of individuals and variables are computed to have logistic responses along the biplot dimensions. The methods are related to logistic regression in the same way as classical biplot analysis (CBA) is related to linear regression, thus are referred as logistic biplot (LB). Most of the estimation methods are developed for matrices in which the number of individuals is much higher than the number of variables. When the number of variables is high, external logistic biplots can be used; row coordinates are obtained by principal coordinates analysis and then logistic regression is fitted to obtain the variables representation. In this work, external logistic biplots for binary data are extended to nominal and ordinal data using parametric and nonparametric logistic fits and then combined in a single representation.