Sentence examples for using the glm was from inspiring English sources

To elucidate the independent effects of age and presence of diabetes, multivariate analysis using the GLM was employed.

The GLM was performed using the following three covariates: PCs, location (0 = Willamette, 1 = Bonneville), and year.

In this second step the GLM was fitted using generalized least-squares in order to correct for temporal correlation of the noise [Burock and Dale, 2000; Wicker and Fonlupt, 2003].

The GLM was fit using iteratively-weighted least squares implemented in the circular package in R. The meta-analysis approach outlined in Fig. 1 yielded a table of scores for the 1000 genes in the master list (Additional file 1), which is one of the primary results of this work.

The GLM was then used to generate the parameter estimates of activity for each voxel, each condition and each participant.

GLMs were fitted using the glm function, while GLMMs were fitted using the glmer function in the lme4 package [ 28].

Logistic regression was conducted using the glm function in R, and the statistical significance of each explanatory variable was evaluated using type III sums of squares (i.e., each factor was evaluated after removing variation attributable to other factors included in the model).

The analysis was conducted using the glm function in R Development Core Team (2010) and model fit was examined with the deviance statistic.

Logistic regression was performed using the glm function in R (Hastie and Pregibon 2007), and model performance was assessed using the nonbinROC package in R (R Core Development Team 2011).

All classical analyses were carried out by using the "glm" function in R. Bayesian analyses were performed by using Markov chain-Monte Carlo techniques implemented in WinBUGS (23), and for which the model code is available from the authors upon request.

All models were run in SAS 9.3 (Cary, NC) using the "GLM" procedure and least squares means were extracted.