Sentence examples for delete variables from inspiring English sources

We note that a researcher may wish to adjust on additional variables to improve precision [ 56] and may wish to delete variables from the final adjustment set based on precision of estimates, as concluded in [ 6].

Some authors have proposed rules for shrinkage methods, suggesting that one could safely evaluate problems when there are at most 10 to 15 times more variables than observations, while others have established criteria to delete variables, for example, Least Absolute Shrinkage and Selection Operator (LASSO) [ 11, 16– 16].

Full models were then reduced by sequentially deleting variables with model P values ≥0.10 to arrive at the final model.

Confounding was determined by comparing the odds ratio in the model with the deleted variables to that of the full model.

The deleted variables were characterized by a very high proportion of missing values, and were related to costs, to physical therapist assistants and to parking availability.

In a backward elimination process (Wald test) we deleted variables from the initial model until only variables with a P value of less than 0.157 (Akaike Information Criterion) were retained in the final model [ 25].

By means of backward elimination, we deleted variables from the initial model until only variables with a P value of <0.157 (Akaike information criterion) were maintained in the model.

The adjusted model was created in a manual stepwise backward elimination procedure starting with the full model and deleting variables one at a time until only significant variables were left in the model.

Measurement error can also cause an estimate to change when adding or deleting variables to or from the adjustment set, even though this would not be the case had the variables been measured perfectly.

This procedure involves deleting variables to a desired degree of accuracy based on contribution to model R. We will compare the reduced and full models using internal bootstrap validation, with appropriate penalisation for the variable selection.

If its deletion resulted in another variable that had been significant (p < 0.10) previously becoming non-significant, then the deleted variable was added back into the model and the variable with the next largest p-value greater than 0.10 was deleted.