:orphan: 





.. _iv_two_cmp_cl_isv_tut:



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

[Generated automatically as a Tutorial summary]

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


:Name: iv_two_cmp_cl_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_cl; proportional noise; additive noise

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

:Diagram: 


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


Comparison
**********



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


.. code-block:: pyml

    -1688.3290



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


.. code-block:: pyml

    -1654.2501



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[CL] = 2.3258
    f[V1] = 7.3693
    f[Q] = 1.0653
    f[V2] = 10.1994
    f[PNOISE] = 0.0123
    f[ANOISE] = 0.0093
    f[CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.1914, 0.2094, -0.0294, -0.0940 ],
        [ 0.2094, 0.6863, -0.0546, 0.1520 ],
        [ -0.0294, -0.0546, 0.1462, 0.0294 ],
        [ -0.0940, 0.1520, 0.0294, 0.6832 ],
    ]



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


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


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


* Gen: :ref:`iv_two_cmp_cl_isv_gen` (gen)
* Fit: :ref:`iv_two_cmp_cl_isv_fit` (fit)

Inputs
******



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

.. code-block:: pyml

    f[CL] = 2.0000
    f[V1] = 10.0000
    f[Q] = 1.2000
    f[V2] = 20.0000
    f[PNOISE] = 0.0100
    f[ANOISE] = 0.0100
    f[CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.1000, 0.0000, 0.0000, 0.0000 ],
        [ 0.0000, 0.2000, 0.0000, 0.0000 ],
        [ 0.0000, 0.0000, 0.1000, 0.0000 ],
        [ 0.0000, 0.0000, 0.0000, 0.5000 ],
    ]



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

.. code-block:: pyml

    f[CL] = 1.0000
    f[V1] = 15.0000
    f[Q] = 1.0000
    f[V2] = 25.0000
    f[PNOISE] = 0.0500
    f[ANOISE] = 0.0500
    f[CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.2000, 0.0000, 0.0000, 0.0000 ],
        [ 0.0000, 0.1000, 0.0000, 0.0000 ],
        [ 0.0000, 0.0000, 0.0500, 0.0000 ],
        [ 0.0000, 0.0000, 0.0000, 0.2000 ],
    ]

