Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

The analysis of data generated by animal habitat selection studies, by family studies of genetic diseases, or by longitudinal follow-up of households often involves fitting a mixed conditional ...

As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems. Logistic regression is a technique used to make ...

Outcome-dependent sampling increases the efficiency of studies of rare outcomes, examples being case-control studies in epidemiology and choice-based sampling in econometrics. Two-phase or double ...

SimpleNomo, an open-source Python Toolbox, and an online platform that generates nomograms directly from logistic regression coefficients and the range of variables are available through a recent ...

Single nucleotide polymorphism (SNP) interaction plays a critical role for complex diseases. The primary limitation of logistic regressions (LR) in testing SNP–SNP interactions is that coefficient ...

The following six machine learning models were developed to predict ADH upgrade from core needle biopsy: gradient-boosting trees, random forest, radial support vector machine (SVM), weighted K-nearest ...