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# Covariates

### Purpose

The figure displays the estimators of the individual parameters in the Gaussian space, and those for random effects, (e.g. the conditional expectations $E(\phi_{i,l}/y;\hat{\theta})$ and $E(\eta_{i,l}/y;\hat{\theta})$ for i from 1 to N and the conditional modes) v.s. the covariates.

### Example of graphic

In the proposed example, the parameters estimation for a PKPD model on the warfarin data set is presented. The random effects of 3 parameters of the PD model are displayed w.r.t. a transformed version of the weight (t_wt=log(wt/70)), the weight, and the sex category.

### Settings

• Grid arrange. Define the number of individual parameters (or random effects) and covariate that are displayed. The user can define the number of rows and the number of columns.
• Display
• The user can choose to see either the individual parameters or the Random effects.
• Data
• Splines: add/remove the a spline interpolation
• Regression lines: add/remove the affine fit