Sentence examples for models comprising only from inspiring English sources

The non-significant variables were excluded stepwise to yield models comprising only significant factors (p <.05).

Likelihood ratio tests were used to compare full models with null models (comprising only of sequence order and the random effect), and Markov Chain Monte Carlo sampling using the functions pvals.fnc and aovlmer.fnc of the R package languageR (Baayen 2011) was applied to calculate P values for the different levels for behaviours that differed between playback conditions.

Bones were then decalcified and reanalyzed by microCT; a second model (comprising only the vessels) was obtained and overimposed on the former, thus providing a clear visualization of vessel trajectories in the invaded metaphysic allowing quantitative evaluation of the vascular volume and vessel diameter.

The nonsignificant variables were excluded sequentially to yield a model comprising only the significant factors (P < 0.05).

We used a likelihood ratio test (ANOVA using "Chisq" argument) to compare the full models with a null model (comprising only the intercept and the random effect) in order to calculate the overall effect of the predator model.

For mortality and transition to long-term care, the AUCs of the models including an FI were significantly higher than the AUCs of a model comprising only age, gender and co-morbidity (p < 0.03).

They were included in a multiple model, from which the variables with P values of 0.05 or higher were excluded stepwise to yield a model comprising only variables with P values < 0.05.

They were included in a multiple model, from which the variables with p-values of.05 or higher were excluded step-wise to yield a model comprising only variables with p-values <.05.

The significance of the full model as compared to the null model (comprising only the intercept and the random effect) was established using a likelihood ratio test (R function Anova with argument test set to "χ").

For hospitalization, the AUC of the full model with age, gender, co-morbidity and an FI was significantly higher than the AUC of a model comprising only age and gender (p < 0.001) [ 37].

Also, log-likelihood ratio χ statistics comparing the model with the combined variable (model 5) with a model comprising only baseline characteristics (age, sex, height, BMI, hypertension, diabetes and palpitation), the model with the combined variable gave a significantly better fit (p<0.0001).