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Aside from these, and unless otherwise noted, P values for tests of proportions were modeled using binomial distributions.
Two other approaches: Maq (Li,H. et al., 2008) and SOAPSNP (Li,R. et al., 2008) have proposed using Binomial distributions to model genotypes; however, these were developed in the context of sequencing normal genomes, not cancer genomes.
We did not estimate separate model parameters for process variance or observation error because we represented uncertainty using binomial distributions.
Differential expression was calculated based on a model using the negative binomial distribution as implemented in the DESeq2 package [ 24].
Thus, we developed a model that uses a negative-binomial distribution to approximate an overdispersed Poisson distribution.
The observed presence/absence data from the nine pinches in each quadrat were modelled using a binomial distribution with probability Py,s,q and number of samples n = 9.
Coral abundances were modelled using negative binomial distribution, as a number of coral colonies was recorded each year in an additive fashion.
It is recommended that you model this outcome with a generalized linear mixed model, using a binomial error distribution.
The data was modeled using a negative binomial distribution.
Survival was modeled using a binomial error distribution.
The GEE models were estimated using a binomial distribution, independent working correlation matrix and a logit link function.
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