Comparison of logistic regression and linear discriminant analysis: a simulation study

01/2004; 1:143-161.

ABSTRACT 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.

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    ABSTRACT: Methods for identifying dyslexia in adults vary widely between studies. Researchers have to decide how many tests to use, which tests are considered to be the most reliable, and how to determine cut-off scores. The aim of this study was to develop an objective and powerful method for diagnosing dyslexia. We took various methodological measures, most of which are new compared to previous methods. We used a large sample of Dutch first-year psychology students, we considered several options for exclusion and inclusion criteria, we collected as many cognitive tests as possible, we used six independent sources of biographical information for a criterion of dyslexia, we compared the predictive power of discriminant analyses and logistic regression analyses, we used both sum scores and item scores as predictor variables, we used self-report questions as predictor variables, and we retested the reliability of predictions with repeated prediction analyses using an adjusted criterion. We were able to identify 74 dyslexic and 369 non-dyslexic students. For 37 students, various predictions were too inconsistent for a final classification. The most reliable predictions were acquired with item scores and self-report questions. The main conclusion is that it is possible to identify dyslexia with a high reliability, although the exact nature of dyslexia is still unknown. We therefore believe that this study yielded valuable information for future methods of identifying dyslexia in Dutch as well as in other languages, and that this would be beneficial for comparing studies across countries.
    Annals of Dyslexia 12/2013; · 1.48 Impact Factor
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    ABSTRACT: In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters.
    PLoS ONE 01/2014; 9(8):e104735. · 3.53 Impact Factor
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    ABSTRACT: Citation: Rahman K, Bowen A, Muhajarine N (2014) Examining the Factors that Moderate and Mediate the Effects on Depression during Pregnancy and Postpartum. J Preg Child Health 1: 116. doi:10.4172/jpch.1000116 Copyright: © 2014 Rahman K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Introduction Maternal depression encompasses a spectrum of depressive conditions that can affect expectant mothers and those up to twelve months postpartum [1]. Estimates of antenatal and postpartum depression in the general population range from 12 to 20% [2,3]. Antenatal depression is a relatively new area of study compared to postpartum depression and the depth and sophistication of this research is still developing. Studies have found that the prevalence of antenatal depression could be higher than postpartum depression [4]. We also noted higher prevalence of antenatal depression (14.1% in early pregnancy; 10.4% in late pregnancy) than postpartum depression (8.1%) [5]. Maternal depression has both immediate and longer-term consequences. Mothers with depression may have diminished capacity for self-care, as well as care for her infant [6]. They reported more sleep disturbances, and anxiety [7]. They were likely to have less frequent antenatal care [6], and reduced optimal fetal monitoring during pregnancy [8]. Antenatal depression was associated with preterm delivery [9], lower birth weight, and small for gestational age [10]. Studies have found maternal postpartum depression to hamper a child's cognitive, emotional, and social development in infancy and early childhood [11-13]. Given the high prevalence and serious consequences of antenatal and postpartum depression, are view of the empirical literature revealed a range of antecedent risk factors, but very little reported on the specific role of the risk factors, for example either as moderating or mediating role on depression. Studies examining mediating or moderating role of the antecedent risk factors in relation to antenatal and postpartum depression is relatively rare in epidemiological research. A mediator is defined as an intermediate variable that accounts for the relationship between predictor and outcome variable [14]. Mediators attempt to describe 'why' and 'how' effects occur [14]. In behavioral research, psychosocial variables such as social support and self-efficacy are often hypothesized as mediating roles [15]. Moderator variables, on the other hand, specify the conditions under which the variable exerts its effect, such as ethnicity and gender [14]. Moderators attempt to describe 'when' and in 'whom' effects may occur. Understanding the mediating and moderating role of risk factors in predicting maternal depression could not only contribute to explain the mechanism of depression, but also greatly enable us to intervene to minimize the harmful effects of depression by focusing on certain factors or on certain patient groups. We hypothesized that socio-demographic factors such as younger maternal age, Aboriginal ethnicity, low education, low income, and single mother status will increase the depression status in late pregnancy and early postpartum Abstract Background: This research report will address the knowledge gap in understanding the role of risk factors as moderators or mediators to explain the variability in the magnitude of exposure and the causal pathway for antenatal and postpartum depression.
    Journal of pregnancy and Child Health. 10/2014; 1(2).

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