Observed data


The purpose of this plot, also called a spaghetti plot, is to display the original data w.r.t. time.


 In the example below, the concentration of warfarin from the warfarin data set is displayed. A subject is highlighted in yellow by hovering on the line.

One can plot the output in a log-scale to have a better evaluation of the elimination part for example as in the figure below.

An interesting feature is the possibility to display the dosing time as on the figure below. In the proposed example (PKVK_project of the demos), the individual dosing time of the individual is displayed when the user hovers an individual.

Information are also provided. We propose

  • The total number of subjects
  • The average number of doses per subject
  • The total, average, minimum and maximum number of observations per individual.

In addition, if we split the graphic with a covariate, all the information are recomputed to manage the information of the group as in the following plot.


  • General: Add/remove the legend or the grid,
  • Axes: Add/remove log-scale, modify labels,
  • Stratify: Split, color and filter by covariates,
  • Preferences: Add/remove elements or change colors and sizes for axes, observations, censored (BLQ) observations, highlighting.

Best practices

  • It is always good to have a look first at the spaghetti plot before running the parameter estimation. Indeed, it is very convenient to see if all the data is consistent, or if some outliers appear. Moreover, looking at the plot can help to identify hypotheses about the model, such as covariate effects.
  • It is possible to generate the Spaghetti plot just after loading the data. For that, click on “Show dataviewer” next to the data file choice.
  • For a better understanding and/or exploration of the data set, it is also possible to export the data set in Datxplore.