Sentence examples for model predictors by from inspiring English sources

We explored the choice of model predictors by comparing nested submodels of the model shown in Figure 2, to determine whether the model could be simplified without loss of predictive accuracy.

To the final model, predictors were selected by forcing all those variables that were significant in pulmonary tuberculosis, and from other variables selection was by forward stepwise method.

Potential model predictors were recoded as required by collapsing sparsely populated levels.

The visualizations (Figure 3) show that the variation unexplained by the fixed model predictors is very large, both among stands and among seedlings within a stand.

Spatial autocorrelation was present in the PM2.5 LUR model residuals (Moran's I = 0.33, p < 0.001), indicating that a moderate amount of spatial autocorrelation remained that was not explained by the PM2.5 model predictors.

The second analysis included all SNPs in a gene simultaneously and compared that model to a model without any predictors by means of a likelihood ratio test [ 30, 31].

With ICE the imputation model of a single variable uses all the other variables as predictors by appropriate regression models (i.e. linear, logistic or multinomial if imputed variable is continuous, dichotomous or categorical).

Among the 24 factors identified as c-section predictors by the full model, only seven were the actual confounders used in the parsimonious model.

We compared the selection of predictors by both models.

With this procedure, the imputation model of a single missing variable used the information from all the other variables considered as predictors by appropriate regression models (i.e. linear or logistic if variable to be imputed was continuous or dichotomous, respectively).

We calculated the independent contribution of each variable included in the prediction model by dividing the difference in the −2 log likelihood of the null model and the final model without the particular predictor by the difference in the −2 log likelihood of the null model and the complete final model [ 43].