We compare these candidate definitions and estimators when a hierarchical logistic regression model is assumed for the binary outcome. Our simulations revealed important differences across reliability estimators
Regression models for clustered data and other hierarchical data structures are described in the article on hierarchical linear model in this volume. Regression models for highly non-normal response variables (e.g., binary responses) are covered in the article on generalized linear models in this ...
andbeyondeachgroupofindependentvariables. Twocategoricalvariableswerecodedtorepresentabinarycode. Binarycodesestablishedweregender(coded1iffemaleand0ifmale),andrace/ethnicity(codedtorepresent1ifWhiteand0otherwise). Infirststepofthehierarchicalregressionprocedure,thedemographicpredictorvariables,age,gender,andrace/...
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circuits in the same family achieve better accuracy and can be used to classify highly entangled quantum states, for which there...
4). Binary logistic regression analyses found evidence of an effect of both age and country. Older children were more likely to state that Dimo feared the dominant character more than the prestigious (OR = 1.30, 95%CI = 1.17–1.44, p < .0001) and children in both Finland (OR = 2.48,...
Logistic discriminant analysis is a form of discriminant analysis based on the assumption that the likelihood ratios of the groups have an exponential form. Multinomial logistic regression provides the basis for logistic discriminant analysis. Because multinomial logistic regression can handle binary and ...
The analytical method used in this research is multilevel binary logistic regression model with two levels. In this research, we considered three multilevel regression models. Empty model The empty two-level model for a dichotomous outcome variable refers to a population of groups (level-two units...
Indeed, recent work has shown that humans can track a contextual binary variable embedded in noise that partially informs about what specific actions need to be performed to obtain reward7. Additionally, humans can infer the transition probability between two stimuli where the transition probability it...
These powerful models will allow you to explore data with a more complicated structure than a standard linear regression. The course then teaches generalized linear mixed-effect regressions. Generalized linear mixed-effects models allow you to model more kinds of data, including binary responses and ...
Micro-F1 is calculated using the binary predictions of all classes. For example, classifying m instances to n classes produces m × n binary predictions, from which Micro-F1 can be computed with Equation (9) and Equation (10). Macro-F1 and Micro-F1 have been two standard evaluation ...