What kind of plots can be generated by Monolix?
The list of plots below corresponds to all the plots that Monolix can generate. They are computed with the task “Plots”, and the list of plots to compute can be selected by clicking on the button next to the task as shown below, prior to running the task.
By default, only the subset of plots are selected, as one can see on this figure. Plots can be selected or unselected one-by-one, by groups or all at once.
In addition to selecting plots, this menu can be used to directly generate one particular plot, by clicking on the green arrow next to it, as can be seen below. The green arrow is not visible if the required information for the chosen plot has not been computed yet. For example, generating the plot “likelihood contribution” requires first to run the “Likelihood” task.
- Observed data: This plot displays the original data w.r.t. time as a spaghetti plot, along with some additional information.
Model for the observations
- Individual fits: This plot displays the individual fits: individual predictions using the individual parameters and the individual covariates w.r.t. time on a continuous grid, with the observed data overlaid.
- Observations vs predictions: This plot displays observations w.r.t. the predictions computed using the population parameters or the individual parameters.
- Scatter plot of the residuals: This plot displays the PWRES (population weighted residuals), the IWRES (individual weighted residuals), and the NPDE (Normalized Prediction Distribution Errors) w.r.t. the time and the prediction.
- Distribution of the residuals: This plot displays the distributions of PWRES, IWRES and NPDE as histograms for the probability density function (PDF) or as cumulative distribution functions (CDF).
Diagnosis plots based on individual parameters
- Distribution of the individual parameters: This plot displays the estimated population distributions of the individual parameters.
- Distribution of the random effects: This plot displays the distribution of the random effects.
- Correlation between random effects: This plot displays scatter plots for each pair of random effects.
- Individual parameters vs covariates: This plot displays the estimators of the individual parameters in the Gaussian space (and those for random effects) w.r.t. the covariates.
Predictive checks and predictions
- Visual predictive checks: This plot displays the Visual Predictive Check.
- Numerical predictive checks: This plot displays the numerical predictive check.
- BLQ predictive checks: This plot displays the proportion of censored data w.r.t. time.
- Prediction distribution: This plot displays the prediction distribution.
- SAEM: This plot displays the convergence of the population parameters estimated with SAEM with respect to the iteration number.
- MCMC: This plot displays the convergence of the Markov Chain Monte Carlo algorithm for the individual parameters estimation.
- Importance sampling: This plot displays the convergence of log-likelihood estimation by importance sampling.
- Likelihood contribution: This plot displays the contribution of each individual to the log-likelihood.
- Standard errors for the estimates: This plot displays the relative standard errors (in %) for the population parameters.
The user can choose to export each plot as an image with an icon on top of it, or all plots at once with the menu Export. It is also possible to export plots data as table, for example to build new plots with external tools.
- the export starts after the display of the plots,
- the plots are exported in the result folder,
- only plot selected in Plots tasks are exported,
- legends and information frames are not exported.
Automatic exporting can be chosen in the project Preferences (in Settings), as well as the exporting format (png or svg):