:orphan: 





.. _iv_two_cmp_isv_tut:



Population Two Compartment Model and Inter-subject Variance
###########################################################

[Generated automatically as a Tutorial summary]

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


:Name: iv_two_cmp_isv

:Title: Population Two Compartment Model and Inter-subject Variance

:Author: PoPy for PK/PD

:Abstract: 

| Population One Compartment Model and Inter-subject Variance

:Keywords: two compartment model; iv_two_cmp_k; proportional noise; additive noise

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

:Diagram: 


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


Comparison
**********



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


.. code-block:: pyml

    1799.2332



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


.. code-block:: pyml

    1795.0662



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[K12] = 0.1893
    f[K21] = 0.1229
    f[KE] = 0.1034
    f[KE_isv,K12_isv,K21_isv] = [
        [ 0.2973, 0.0000, 0.0000 ],
        [ 0.0000, 0.1608, 0.0000 ],
        [ 0.0000, 0.0000, 0.1855 ],
    ]
    f[ANOISE_STD] = 4.9307



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


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


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


* Gen: :ref:`iv_two_cmp_isv_gen` (gen)
* Fit: :ref:`iv_two_cmp_isv_fit` (fit)

Inputs
******



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

.. code-block:: pyml

    f[K12] = 0.2000
    f[K21] = 0.1500
    f[KE] = 0.1000
    f[KE_isv,K12_isv,K21_isv] = [
        [ 0.2000, 0.0000, 0.0000 ],
        [ 0.0000, 0.2000, 0.0000 ],
        [ 0.0000, 0.0000, 0.2000 ],
    ]
    f[ANOISE_STD] = 5.0000



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

.. code-block:: pyml

    f[K12] = 0.5000
    f[K21] = 0.5000
    f[KE] = 0.5000
    f[KE_isv,K12_isv,K21_isv] = [
        [ 0.0100, 0.0000, 0.0000 ],
        [ 0.0000, 0.0100, 0.0000 ],
        [ 0.0000, 0.0000, 0.0100 ],
    ]
    f[ANOISE_STD] = 100.0000

