Sentence examples for by fitting a negative from inspiring English sources

All models will be corrected for possible overdispersion, either by using an adjustment parameter in the Poisson model or by fitting a negative binomial regression model.

We obtained sex-specific and age-group specific MN meage-group specificfitting a negative binoMNal randomeanfrequencies to the MN count of each subyect (Lindsey 1995).

Differential expression was evaluated by fitting a negative binomial generalized linear model for each feature and then adjusting the P-value for multiple testing using the Benjamini-Hochberg correction [ 78] with a false discovery rate (FDR) of 0.05.

Transition probabilities between states are determined by fitting a negative binomial distribution to the recombination site choice for VH, DH and JH and number of n nucleotides in the junctions as determined in a set of 293 unmutated sequences rearranged sequences (Fig. 2, Jackson et al., 2004).

Percent recovery was estimated by fitting a negative binomial regression model to the observed number of oocysts, with the number of spiked or expected oocysts functioning as the offset variable and fecal identification as a cluster variable due to using three replicates per concentration per fecal sample [ 25].

We compared our segmentation with that corresponding to the SGD annotation through the Hellinger distance by fitting a negative binomial distribution on each segment and repeated this comparison with the other two algorithms able to process long count datasets: PELT [ 16] and Binary Segmentation [ 6], both implemented in the R package changepoint for the Poisson distribution.

For these reasons, we ultimately took a Bayesian approach by fitting a multivariate negative binomial reduced rank random regression model using a Metropolis Hastings algorithm implemented in R [ 17].

a — Differential abundance of OTUs in the earliest diverged Oryza species rhizosphere was assessed by fitting a local regression model with a negative binomial distribution to the sequence count data and testing for differential abundance with a likelihood ratio test as implemented in the R package DESeq2 (Love et al. 2014) in conjunction with the Phyloseq package (McMurdie and Holmes 2013).

We conducted analysis of the differential abundance of OTUs in different samples by fitting a local regression model with a negative binomial distribution to the data and testing for differential abundance with a likelihood ratio test as implemented in the R package DESeq2 (Love et al. 2014) and in conjunction with the Phyloseq package (McMurdie and Holmes 2013).

Differential abundance of OTUs in cultivated or wild rice rhizosphere was assessed by fitting a local regression model with a negative binomial distribution to the data and testing for differential abundance with a likelihood ratio test as implemented in the R package DESeq2 (Love et al. 2014) in conjunction with the Phyloseq package (McMurdie and Holmes 2013).

To explore whether the number of editions is influenced by gender, we fit a negative binomial (NB) regression model.