Sentence examples for model building dataset from inspiring English sources

The prediction agreement found between the model binary outputs and the historical fire data was 85.3% for the model building dataset (60% of municipalities).

This analysis was initially performed on the entire model building dataset to identify informative terms for ages 30 through 69.

Such models are expected to improve predictive performance in patients independent of the model building dataset 23.

Post hoc Bayesian forecasting (i.e., MAXEVAL = 0) was used to predict the plasma concentrations for the validation subjects, by fixing parameters in the structural and variance model to the final estimates, incorporating individual dosing history and covariates, and assuming a typical AAG level of 19.32 μM (median value from all HV in the model building dataset).

The methods used to determine CYP3A5 genotype in subjects in the model building datasets have been described previously 6, 7. Deviation from Hardy Weinberg equilibrium was examined using Pearson's χ-test.

Ideally, the model-building dataset should span the entire range of the values to be modeled.

The model-building dataset was then used for visual predictive check simulations (N = 100) using the newly estimated parameters, and CL/F was determined using noncompartmental approaches for both observed and simulated data.

To evaluate model structure, simulated data were plotted against the model-building dataset. 1, 11– 13 After the final model structure was in place and the univariate parameter optimization step was performed, a local sensitivity analysis was conducted on all of the 23 estimated sclerostin-related parameters (Tables 2, 3 ; Supplementary Figure 1).

The combined model building and validation datasets contained 2071 Caucasian cases and 4144 controls between the ages of 30 and 69, with age for cases being their age at diagnosis and age for controls being their age at the time of study enrollment.

Two-thirds of the 1,348 subjects were randomly selected (without regard to matching or case status) as a training dataset for model building, while the remaining one-third was used later as a validation dataset to estimate prediction capabilities.

Genotyping and clinical risk factor data were then combined for each individual in large model building and validation case control datasets consisting of participants enrolled from multiple geographic regions within the US.