:orphan: 





.. _builtin_tut_example_gen:



First order absorption model with peripheral compartment
########################################################

[Generated automatically as a Generation summary]

Model Description
*****************


:Name: builtin_tut_example

:Title: First order absorption model with peripheral compartment

:Author: PoPy for PK/PD

:Abstract: 

| A two compartment PK model with bolus dose and
| first order absorption, similar to a Nonmem advan4trans4 model.

:Keywords: tutorial; pk; advan4; dep_two_cmp; first order

:Input Script: :download:`builtin_tut_example_gen.pyml <builtin_tut_example_gen.pyml>`

:Diagram: 


.. thumbnail:: builtin_tut_example_gen.pyml_output/compartment_diagram.svg
    :width: 200px


Outputs
*******



Individual simulated (sim) plots
================================



.. thumbnail:: images/gen_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/gen_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/gen_sim_grph_outputs/indOBS_vs_TIME/000003.svg
    :width: 200px


Alternatively see :ref:`builtin_tut_example_simulated_sim_plots`

Population simulated (sim) plots
================================


(No population graphs were requested.)

Generated parameter .csv files
==============================


:Fixed Effects: :download:`fx_params.csv (gen) <builtin_tut_example_gen.pyml_output/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (gen) <builtin_tut_example_gen.pyml_output/rx_params.csv>`

:Model params: :download:`mx_params.csv (gen) <builtin_tut_example_gen.pyml_output/mx_params.csv>`

:State values: :download:`sx_params.csv (gen) <builtin_tut_example_gen.pyml_output/sx_params.csv>`

:Predictions: :download:`px_params.csv (gen) <builtin_tut_example_gen.pyml_output/px_params.csv>`


:Observations: :download:`synthetic_data.csv (gen) <synthetic_data.csv>`


Inputs
******



True f[X] values (for simulation)
=================================


.. code-block:: pyml

    f[KA] = 0.2000
    f[CL] = 2.0000
    f[V1] = 50.0000
    f[Q] = 1.0000
    f[V2] = 80.0000
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.1000, 0.0100, 0.0100, 0.0100, 0.0100 ],
        [ 0.0100, 0.0300, -0.0100, 0.0200, 0.0200 ],
        [ 0.0100, -0.0100, 0.0900, 0.0100, 0.0100 ],
        [ 0.0100, 0.0200, 0.0100, 0.0700, 0.0100 ],
        [ 0.0100, 0.0200, 0.0100, 0.0100, 0.0500 ],
    ]
    f[PNOISE] = 0.1500

