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This workshop is an introduction to non-linear mixed effects (NLME) modeling in Pumas.

It covers how to:

• parse data into a Population
• define models with @model by specifying:
  • parameters with @param
  • random effects with @random
  • individual coefficients and statistical transformations with @pre
  • model dynamics with @dynamics
  • error models with @derived
• perform an estimation with fit and accounting for:
  • different estimation methods such as FOCE and LaplaceI
  • fixed parameters values
• calculate confidence intervals with infer by using:
  • variance-covariance matrix
  • bootstrap
  • sampling importance resampling (SIR)

The following Julia files are provided:

1. 01-population.jl: covers how to define Populations from DataFrames
2. 02-model.jl: walks through the @model syntax and the model blocks
3. 03-fit.jl: an overview of different usages of the fit function
4. 04-infer.jl: an overview of different usages of the infer function

Prerequisites

We recommend users being familiar with Julia syntax, especially variables and types.

The formal requirements are the Julia Syntax Workshop and its pre-requisites.

Schedule

Time (HH:MM)	Activity	Description
00:00	Setup	Download files required for the workshop
00:05	Parsing Data	Showcase 01-population.jl
00:20	Model Specification	Showcase 02-model.jl
00:35	Model Fitting	Showcase 03-fit.jl
00:45	Model Confidence Intervals	Showcase 04-infer.jl
00:55	Closing Remarks	See if there are any questions and feedback

Get in touch

If you have any suggestions or want to get in touch with our education team, please send an email to training@pumas.ai.

Authors

• Jose Storopoli - jose@pumas.ai

License

This content is licensed under Creative Commons Attribution-ShareAlike 4.0 International.