Sentence examples for predictors in the logistic regression from inspiring English sources

The most important predictors in the logistic regression were leg measures, head measures, attention shifts and errors of omission.

It treats the multiplicative term between SNPs, along with individual SNP terms, as predictors in the logistic regression model.

Due to variability in data availability across years, not all hospitalization characteristics were included as predictors in the logistic regression to estimate propensity scores.

Collinearity diagnostics indicated that all predictors in the logistic regression model made unique contributions to classification of smoking status in the ALC cohort.

Among all factors potentially associated with the outcome under study, age and gender were considered a priori risk factors; the others were selected using a stepwise bootstrap procedure to assign an importance rank of predictors in the logistic regression analysis.

For the co-morbidities, a bootstrap stepwise procedure that assigned an importance rank for the predictors in the logistic regression was implemented to identify the set of conditions that significantly predicted the risk of the outcome while optimising the trade-off between the goodness-of-fit of the final model and parsimony.

First, the estimated person location was the only predictor in the logistic regression model.

Female gender was, however, not significantly associated with CA in the univariate analysis, which may have been due to suppressor effects of unknown confounders in the univariate analysis, or by more precise estimation of gender as a predictor in the logistic regression model due to the presence of another strong predictor in the model 38.

Using the coefficients assigned to each predictor in the logistic regression model, we derived a prediction model as follows: logit (predicted probability of infant with EONI) = -4.047 + 0.055*CRP + 0.013*[(WBC count/10000)^7.6354 - 1.660, where EONI was coded (0) if absent and (1) if present.

The first two axes of the PCA were used as orthogonal predictors in the logistic regressions.

The parameter estimation step of MRF-Deng is problematic in that proteins with known function ("annotated proteins") have unannotated proteins as neighbors so that the predictors used in the logistic regression carry uncertainty due to the unannotated proteins (Figure 1).