:orphan: 





.. _indirect_pd_pop_tut:



Indirect_PKPD_model
###################

[Generated automatically as a Tutorial summary]

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


:Name: indirect_pd_pop

:Title: Indirect_PKPD_model

:Author: PoPy for PK/PD

:Abstract: 

| A indirect (i.e. uses delay compartments) PD Model, based on the amount of drug in the body, delayed by two lag compartments
| The amount in the central compartment is determined by K, which has been estimated for each individual.
| The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

:Keywords: pd; one compartment model; indirect; delay compartment

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

:Diagram: 


.. thumbnail:: compartment_diagram.svg
    :width: 200px


Comparison
**********



True objective value
====================


.. code-block:: pyml

    3940.3541



Final fitted objective value
============================


.. code-block:: pyml

    3933.3780



Compare Main f[X]
=================



.. csv-table:: 
    :file: fx_comp_main.csv
    :header-rows: 1


Compare Noise f[X]
==================



.. csv-table:: 
    :file: fx_comp_noise.csv
    :header-rows: 1


Compare Variance f[X]
=====================



.. csv-table:: 
    :file: fx_comp_variance.csv
    :header-rows: 1


Outputs
*******



Fitted f[X] values (after fitting)
==================================


.. code-block:: pyml

    f[BASE] = 781.9491
    f[KOUT] = 0.0286
    f[PNOISE] = 0.0967
    f[ANOISE] = 0.2009
    f[BASE_isv,KOUT_isv] = [
        [ 0.0345, 0.0047 ],
        [ 0.0047, 0.0407 ],
    ]



Generated data .csv file
========================


:Synthetic Data: :download:`synthetic_data.csv <synthetic_data.csv>`


Gen and Fit Summaries
=====================


* Gen: :ref:`indirect_pd_pop_gen` (gen)
* Fit: :ref:`indirect_pd_pop_fit` (fit)

Inputs
******



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

.. code-block:: pyml

    f[BASE] = 800.0000
    f[KOUT] = 0.0300
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.5000
    f[BASE_isv,KOUT_isv] = [
        [ 0.0500, 0.0000 ],
        [ 0.0000, 0.0100 ],
    ]



Starting f[X] values (before fitting)
=====================================

.. code-block:: pyml

    f[BASE] = 500.0000
    f[KOUT] = 0.1000
    f[PNOISE] = 0.0500
    f[ANOISE] = 0.2000
    f[BASE_isv,KOUT_isv] = [
        [ 0.0200, 0.0000 ],
        [ 0.0000, 0.0200 ],
    ]

