Sentence examples for binomial models were fit from inspiring English sources
First, zero-inflated negative binomial models were fit to the data; this modelling strategy can be useful for overdispersed count data, when two separate processes are postulated to contribute to observed counts (one process driving the count outcomes and a second process generating excess zeroes).
Zero-inflated negative binomial models were fit to the data to assess for evidence of a separate process generating excess zeroes.
As over-dispersion of the extracted count data would make the use of the Poisson distribution inappropriate, negative binomial models were fitted first, as this distribution estimates a dispersion parameter.
Thereafter, Bayesian negative binomial models were fitted in WinBUGS [ 48] to examine the association between non-alcoholic liver disease deaths and bloom coverage using a conditional autoregressive (CAR) process.
Log binomial models were fitted to assess the overall association between PCBs and diabetes because the prevalence of diabetes is 27%; ORs are more difficult to interpret than relative risks when the prevalence of the outcome is > 10 20% because they overestimate the effects.
In the first, generalized linear models with log link functions and binomial error distributions (log-binomial models) were fit.
In these comparisons, negative binomial models are fitted to the transcript abundancies determined by RSEM.
The logistic and negative binomial models are fitted in Stata/SE version 10.1 software (StataCorp, College Station, TX, USA).
After negative binomial models are fitted and dispersion estimates are obtained, it is possible to proceed with the tests for determining differential expression using a generalized linear model (GLM) likelihood ratio test.
Log-binomial models were fitted to continuous risk factors.
