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Check initial estimates and auto init

Check initial fixed effects

The subtab “Check initial estimates” is part of the tab “Initial estimates“.

When clicking on the “Check the initial estimates”, the simulations obtained with the initial population fixed effects values and the individual designs (doses and regressors) are displayed for each individual together with the data points, in case of continuous observations. This feature is very useful to find some “good” initial values. Although Monolix is quite robust with respect to initial parameter values, good initial estimates speed up the estimation. You can change the values of the parameters on the bottom of the screen and see how the agreement with the data change. In addition, you can change the axis to log-scale and choose the same limit on all axes to have a better comparison of the individuals. When you are confident with the initial values, you should click on the “SET AS INITIAL VALUES” button on the top of the frame to validate the selection.

In addition, if you think that there are not enough points for the prediction (if there are a lot of doses for example), you can change the discretization and increase the number of points as displayed in the blue box of the figure.

If several observation ids have been mapped to model outputs (for example a parent and a metabolite, or a PK and a PD observation), you can select which output to look at under the output section on the right:


Using the reference in the “check initial estimates”

Starting from the 2019 version, it is possible to add a reference and thus change a parameter to see the impact of the variation of this parameter. In this example, we click on reference to use the current fit as reference and change k12 from 1 to 2 as can be seen on the following figure.



The solid red curve corresponds to the current curve and the dashed one corresponds to the reference. At any time, you can change the reference to use the current fit, and restore and delete the reference or delete all references by clicking on the icons at the top right of the frame.


Automatic initialization of the parameters

Starting from the 2021 version, an auto-init section appears on the right side of the frame:


By clicking on RUN, Monolix will compute initial population parameters that best fit the data points, starting from parameter values currently used in the initial estimates panel, and using the data from the 12 first individuals by default, and from all observations mapped to a model output. It is possible to change the set of individuals used in the Ids selection panel just below the RUN button.

The computation is done with a custom optimization method, on the pooled data, without inter-individual variability. The purpose is not to find a perfect match but rather to have all the parameters in the good range for starting the population modeling approach. While auto-init is running, we show the evolution of the cost of the optimization algorithm over the iterations. It is possible to stop the algorithm at any time if you find the cost has decreased sufficiently and you want to have a look at the parameter values. Note that the more individuals are selected, the longer the run will take. Moreover, it may be easier for the auto-init algorithm to find a point in the parameter space that is sensitive to specific model features (eg a third compartment, a complex absorption) if you select only a few individuals (1-3 for instance) for which this feature can be observed.



After clicking on the button, the population parameters are updated and the corresponding fit is displayed. To use these parameters as initial estimates, you need to click on the button “SET AS INITIAL VALUES”. A new reference appears with previous parameter values so that you can come back to previous values if you are not satisfied with the fit.

Note that the auto-init procedure takes into account the current initial values. Therefore, in the few cases where the auto-init might give poor results, it is possible to improve the results by changing manually the parameter values before running the auto-init again.