Comparison of Logistic Regression and Linear Discriminant Analysis: A Simulation Study
نویسندگان
چکیده
Two of the most widely used statistical methods for analyzing categorical outcome variables are linear discriminant analysis and logistic regression. While both are appropriate for the development of linear classification models, linear discriminant analysis makes more assumptions about the underlying data. Hence, it is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions. In this paper we consider the problem of choosing between the two methods, and set some guidelines for proper choice. The comparison between the methods is based on several measures of predictive accuracy. The performance of the methods is studied by simulations. We start with an example where all the assumptions of the linear discriminant analysis are satisfied and observe the impact of changes regarding the sample size, covariance matrix, Mahalanobis distance and direction of distance between group means. Next, we compare the robustness of the methods towards categorisation and non-normality of explanatory variables in a closely controlled way. We show that the results of LDA and LR are close whenever the normality assumptions are not too badly violated, and set some guidelines for recognizing these situations. We discuss the inappropriateness of LDA in all other cases.
منابع مشابه
Using Classic Discriminant Analysis and Detection Function for Separation of Chemical Victims in Sardasht City to Exposed and Non Exposure Mustard Groups in 2013 and Comparison with Logistic Regression
متن کامل
Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis
Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods : In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were ...
متن کاملComparison of Gestational Diabetes Prediction Between Logistic Regression, Discriminant Analysis, Decision Tree and Artificial Neural Network Models
Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these m...
متن کاملComparing Discriminant Analysis, Ecological Niche Factor Analysis and Logistic Regression Methods for Geographic Distribution Modelling of Eurotia ceratoides (L.) C. A. Mey
Eurotia ceratoides (L.) C. A. Mey is an important plant species in semi-arid landsin Iran. New approaches are required to determine the distribution of this plant species. Forthis reason, geographical distributions of Eurotia ceratoides were assessed using threedifferent models including: Multiple Discriminant Analysis (MDA), Ecological Niche FactorAnalysis (ENFA) and Logistic Regression (LR). ...
متن کاملLogistic Regression and Bayesian Model Selection in Estimation of Probability of Success
Logistic regression and linear discriminant analysis are used to estimate probability of success for binary data based on a training sample and a certain amount of prior information. Posterior probabilities of success are calculated for different choices of the model for the training sample distribution. An approach to model selection is suggested for certain classes of distributions based on t...
متن کامل