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The Estimation of Item Response Models with the lmer Function from the lme4 Package in R [PDF]
Journal of Statistical Software, 2011 In this paper we elaborate on the potential of the lmer function from the lme4 package in R for item response (IRT) modeling. In line with the package, an IRT framework is described based on generalized linear mixed modeling. The aspects of the framework
Paul De Boeck +6 more
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Regularization Paths for Generalized Linear Models via Coordinate Descent [PDF]
Journal of Statistical Software, 2010 We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge
Jerome Friedman +2 more
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In Search of Complex Disease Risk through Genome Wide Association Studies
Mathematics, 2021 The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research.
Lorena Alonso +3 more
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Elastic Net Regularization Paths for All Generalized Linear Models
Journal of Statistical Software, 2023 The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the elastic net regularization path for ordinary least ...
J. Kenneth Tay +2 more
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PLoS ONE, 2021 Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences.
Chénangnon Frédéric Tovissodé +2 more
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Generalized linear mixed models can detect unimodal species-environment relationships [PDF]
PeerJ, 2013 Niche theory predicts that species occurrence and abundance show non-linear, unimodal relationships with respect to environmental gradients. Unimodal models, such as the Gaussian (logistic) model, are however more difficult to fit to data than linear ...
Tahira Jamil, Cajo J.F. ter Braak
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Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At – Risk Students [PDF]
Alinteri Journal of Agriculture Sciences, 2021 Aim: To predict the accuracy percentage of At - risk students based on High withdrawal and Failure rate. Materials and methods: Logistic Regression with sample size = 20 and Generalised Linear Model (GLM) with sample size = 20 was iterated different times for predicting accuracy percentage of At - risk students.
K. Harini, K. Sashi Rekha
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Evaluating an Automated Number Series Item Generator Using Linear Logistic Test Models [PDF]
Journal of Intelligence, 2018 This study investigates the item properties of a newly developed Automatic Number Series Item Generator (ANSIG). The foundation of the ANSIG is based on five hypothesised cognitive operators. Thirteen item models were developed using the numGen R package and eleven were evaluated in this study.
Bao Loe +3 more
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Frontiers in Public Health, 2023 ObjectiveIndependent and interactive effects of multiple metals levels in urine on the risk of hyperuricemia (HUA) in the elderly were investigated.MethodsA total of 6,508 individuals from the baseline population of the Shenzhen aging-related disorder ...
Chao Huang +12 more
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Forecasting Prorocentrum minimum blooms in the Chesapeake Bay using empirical habitat models
Frontiers in Marine Science, 2023 Aquaculturists, local beach managers, and other stakeholders require forecasts of harmful biotic events, so they can assess and respond to health threats when harmful algal blooms (HABs) are present.
Dante M. L. Horemans +4 more
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