Sentence examples for binomial regression coefficient the from inspiring English sources
For a negative binomial regression coefficient, the difference in the logs of expected counts of the response variable is expected to change by the respective regression coefficient for each unit increase in the predictor, given the other predictor variables in the model are held constant.
Table 7 presents the negative binomial regression coefficients for variables in the models identified as the Akaike 'best-ranked' models.
The negative binomial regression coefficients, along with their associated confidence interval values, were exponentiated to produce incidence rate ratios (IRR) – a ratio based on the incidence of counts.
The Pearson's correlation coefficient between the regression coefficients obtained from analysis using quasi-likelihood estimation and that from analysis using negative binomial models were 0.99 for MEI lag 1-11 months, and 0.98 for SLP lag 1-11 months, respectively.
For the two-part joint mixed model (binomial/Poisson) the regression coefficients changed from −0.18(3 months) and −0.27 (6 months) to 0.12 (3 months) and 0.25 (6 months).
The interpretation of the regression coefficient of the two-part joint mixed model is somewhat more complicated, since the model gives a combined regression coefficient for the binomial process and the Poisson process.
'Border modulation' was defined as the maximal regression coefficient among the four coefficients associated with each wall.
Adding random slopes for time to the models changed the sign of the regression coefficient for both the Poisson mixed model and the two-part joint mixed model (binomial/Poisson) and resulted into a much better fit.
In the present paper a two-part joint model was used to model the number of hypoglycaemic events obtaining a shared regression coefficient for both the binomial and the Poisson distribution combined.
Data were analysed with generalised negative binomial regression, controlling for the effect of sampling period.
