Sentence examples for model excluding variables from inspiring English sources

Because the household information was not available for all the children in the study, we repeated this model excluding variables on household confounders.

The best predictors of total cost will be those variables that remain in the final model (excluding variables which were forced into the final model that did not statistically improve the fit of the model or were not confounding factors).

The best predictors of each outcome variable of interest (health and service use) will be those variables that remain in the final GEE models (excluding variables which were forced into the final model that did not statistically improve the fit of the model or were not confounding factors).

For example, newer random forest models exclude variable radius plot locations that had biomass detected by LIDAR over a 30-m area (the pixel size) but that had no trees measured in them.

In all four models all predictor variables were kept in the model, as the AIC indicated no improvement of the models when excluding variables.

To circumvent the risk of an overfitted model, which can arise when there are excess potential explanatory variables, as is often the case with biomarkers, we have used bootstrap sampling to refine the models by excluding variables with an unstable form or those that are unreliably included as necessary for prediction.

Model 1 – Model 8: sequentially excluding variables with least association with mammography history aIndicates continuous variable bIndicates categorical variable The first and largest cluster (N = 451) comprised older women (mean age 52 years), all of whom have had a mammogram previously (mean age of first mammogram 44 years).

A fourth model was developed excluding variables most likely to change over the course of admission (ie, clinical variables), giving the model utility for variables collected at any time throughout the hospital stay.

Stepwise algorithms use as a starting model either the intercept-only or the full model and build on this model by introducing or excluding variables one by one with no reference to other variables until the best model is selected according to an information criterion.

We determined selection of risk factors for inclusion in the final multivariable prediction model by backwards selection, excluding variables that had P>0.1 in the multivariate model.

All determinants with P values of < 0.10 were entered together in the full model of logistic regression, and the model was reduced by excluding variables with P values of > 0.10.