Sentence examples for parameters in the full model from inspiring English sources

The difference of minus two times the log-likelihood (-2lnL) for a reduced model and that of the full model is approximately χ2 distributed with degrees of freedom (df) equal to the difference of the number of estimated parameters in the full model and the number of estimated parameters in the reduced model.

There are six free parameters in the full model, which theoretically give us 64 submodels.

The confidence intervals of the parameters in the full model based on the leverage analysis were comparable to the intervals given in Table 4.

Typically H0 fixes the values of d parameters in the full model H1 which allows those parameters to vary freely in an open neighbourhood.

The simplification of the IIV structure enabled the establishment of a stable base model (as determined by condition number <1000 and correlation <0.95 in the parameter standard error matrix), which was essential for the robust estimation of covariate effects on PK parameters in the full model.

In this procedure, a particular covariate parameter in the full model was fixed to its null value, and the model was run to obtain a new OFV.

A negative percent difference indicates that the parameter functions in the full model to increase variability, and a positive difference indicates that the parameter functions to decrease variability.

11 parameters were optimized in the full model, 10 in the control model (see values in Table 3).> The control model was used to provide a baseline for assessing the performance of other models.

There were n = 2555 data points, 365 days of case data for each of seven years, and k, the number of parameters estimated was 28 in the full model (four parameters for seven years) and 16 in each other model (two parameters for seven years and two parameters overall).

This is because, while the null model is nested in the full model, parameters of the full model take bounding values in the null case.

In the Discussion, we consider alternative schemes that may allow estimating all parameters of the full model.