The normalized probe intensities were summarized into gene expression levels by using an additive model fitted by the median polish procedure [ 76, 77].
All genetic analyses were performed using additive genetic models [ 10].
We predicted maternal status (parturient versus non-parturient) by using generalized additive models (GAMs) to parameterize response curves depicting daily movement of elk during calving season.
By using generalized additive modelling to separate variation in capture rates, we found one component that was unimodally related to capture date and one that was a function of short-term weather fluctuations through the season.
In some studies, temporal correlations were handled by using generalized additive models (GAM) with time and sometimes other variables related to weather were added as smoother variables [ 16, 29].
It was modelled using additive, proportional or combined error models.
Biallelic tests were performed using additive, dominant/recessive genetic models.
Overall, we had 80% statistical power to detect ORs ranging from 1.17 to 1.40 for SNPs with an MAF of 0.05 to 0.49 by using log-additive models and an alpha of 0.05.
We used conditional logistic regression models with strata defined by 5-year age group and the three specimen collection centers to estimate the odds ratios (ORs), 95% confidence intervals (CIs), and P values associated with each SNP by using log-additive models.
The data is then processed by a computer and a 3D model created before being printed using additive manufacturing.
