Sentence examples for using generalized linear and from inspiring English sources

We considered the strength of the relationships between this group of variables and fishing density across all gears using generalized linear and general regression models in STATISTICA.

Demographic and clinical characteristics were compared across regions using generalized linear and mixed models.

Multilevel logistic regression models were carried out using generalized linear and latent mixed models (gllamm) [ 42]; this enabled the simultaneous assessment of the association between individual and community-level factors, IPV, and terminated pregnancy.

Two-level multilevel logistic regression models were fitted using generalized linear and latent mixed models (gllamm) [ 58], to account for the hierarchical nature of DHS data, and to examine the association between each traumatic physical consequence, any IPV, and other predictor variables.

Associations of EPC number and EPC-CFU tertile groups with vascular risk factors, life-style and demographic variables, IMT and severity of atherosclerosis were assessed using generalized linear models and logistic regression analysis.

Differences in the number of polymorphic or fixed bands observed for island and mainland sites were compared using generalized linear models and a Poisson distribution in JMP 9 statistical software (SAS Institute Inc., Cary, NC, USA).

We identified genes and pathways that were differentially methylated using generalized linear models and Ingenuity Pathway Analysis.

All of the statistical analyses were performed using STATA/MP version 10.0 (2007) (Stata Corporation, College Station, TX, USA), and the logistic regression was conducted using generalized linear latent and mixed models (gllamm) method [ 22, 23].

The effects of tree diversity on recruitment were then studied using generalized linear models and universal kriging to account for non-spatial factors and for spatial autocorrelation.

We related alpha diversity to environmental variables using generalized linear models and mapped it from the best-fit models.

Data were analyzed using generalized linear models and results were presented as least squares means ± standard error.