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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.covariates

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
    • Baseline: add/remove the baseline.
    • Informations : add/remove the correlation information for each continuous covariate and each random effect.
    • Legend : add/remove the legend.
  • Estimator. The user can define which estimator is used for the definition of the individual parameters.