For regression models other than the linear model, R-squared type goodness-of-fit summary statistics have been constructed for particular models using a variety of methods. We propose an R-squared measure of goodness of fit for the class of exponential family regression models, which includes logit, probit, Poisson, geometric, gamma and exponential. This R-squared is defined as the proportionat...

Most discussions of ordinal variables in the sociological literature debate the suitability of linear regression and structural equation methods when some variables are ordinal. Largely ignored in these discussions are methods for ordinal variables that are natural extensions of probit and logit models for dichotomous variables. If ordinal variables are discrete realizations of unmeasured conti...

Abstract In cognitive modeling, it is often necessary to complement a core model with choice rule derive testable predictions about behavior. Researchers can typically choose from variety of rules for single model. This article demonstrates that seemingly subtle differences in rules’ assumptions how consistency relates underlying preferences affect the distinguishability competing models’ and, ...

The nonlinear fixed effects model has two shortcomings, one practical, one methodological. The practical obstacle relates to the difficulty of estimating nonlinear models with possibly thousands of dummy variable coefficients. In fact, in many models of interest to practitioners, estimation of the fixed effects model is feasible even in panels with very large numbers of groups. The result, thou...

The nonlinear fixed-effects model has two shortcomings, one practical and one methodological. The practical obstacle relates to the difficulty of computing the MLE of the coefficients of non-linear models with possibly thousands of dummy variable coefficients. In fact, in many models of interest to practitioners, computing the MLE of the parameters of fixed effects model is feasible even in pan...

We show that with a simple normalization of explanatory variables, marginal effects in probit and logit models simplify dramatically, becoming a function of only the estimated constant term. Related simplifications hold for computation of asymptotic variances of these effects. D 2003 Elsevier B.V. All rights reserved.

The use of logistic regression for outcome classification of dichotomous variables is well known in data mining applications. The estimated probability of the logit transformation belongs to the class of canonical link functions that follow from particular probability distribution functions. A closely related model is the probit link which can be used for binary responses. Although the probit l...