Sentence examples for binary response variables were from inspiring English sources

Models of binary response variables were undertaken using the lmer function of the lme4 package [ 59], while female parading and solicitation rate models were undertaken using the MCMCglmm package [ 60], as data were overdispersed.

Multiple binary response variables were defined where each BDA with observed concentrations was assigned an indoor radon vulnerability classification based on the following thresholds: 50, 100, 150, 200, 300, 400, 500, and, 600 Bq m−3.

Binary response variables are oftentimes analyzed using subject and item ANOVAs (F1 and F2) over proportions or percentages.

Model 1 was a logistic regression (function 'glmmPQL') in which the binary response variable was whether a comparison supported the hypothesis or not.

The smoking status binary response variable was constructed from an existing smoking variable with four categories.

The binary response variable is coded as 0 or 1, and the categorical predictors (typically SNP genotypes) are coded numerically (0, 1, 2, etc).

Generalized linear models (GLM) are commonly used to analyse binary data, where the expected value of the binary response variable is linked to the explanatory variables (traits) by a linear equation after applying a link function [ 9].

The binary response variable was the presence or absence of virus antibodies in mouse serum samples, and explanatory variables were province; trapping site; rodent weight, sex, and breeding status by species; rodent abundance index; and trapping year.

A logistic regression model (GENMOD procedure in SAS) for the binary response variable was calculated in the first step of the two-part model predicting the odds ratio of positive expenditures in the respective obesity class.

Binary and categorical response variables were analysed using a logistic regression model similar to that used for the primary analysis.

As the response variables were binary, logistic models were used.