:orphan: 





.. _iv_two_cmp_vss_isv_tut:



Population Two Compartment Model using VSS transform and Inter-subject Variance
###############################################################################

[Generated automatically as a Tutorial summary]

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


:Name: iv_two_cmp_vss_isv

:Title: Population Two Compartment Model using VSS transform 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_vss; proportional noise; additive noise; vss_transform

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

:Diagram: 


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


Comparison
**********



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


.. code-block:: pyml

    -1693.2929



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


.. code-block:: pyml

    -1710.7957



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] = 2.0209
    f[V] = 9.8915
    f[Q] = 1.0753
    f[VSS] = 38.3983
    f[PNOISE] = 0.0079
    f[ANOISE] = 0.0098
    f[CL_isv,V_isv,Q_isv,VSS_isv] = [
        [ 0.1000, 0.0390, 0.0006, -0.0263 ],
        [ 0.0390, 0.1686, 0.0224, 0.0106 ],
        [ 0.0006, 0.0224, 0.0526, 0.0084 ],
        [ -0.0263, 0.0106, 0.0084, 0.3335 ],
    ]



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


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


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


* Gen: :ref:`iv_two_cmp_vss_isv_gen` (gen)
* Fit: :ref:`iv_two_cmp_vss_isv_fit` (fit)

Inputs
******



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

.. code-block:: pyml

    f[CL] = 2.0000
    f[V] = 10.0000
    f[Q] = 1.2000
    f[VSS] = 50.0000
    f[PNOISE] = 0.0100
    f[ANOISE] = 0.0100
    f[CL_isv,V_isv,Q_isv,VSS_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[V] = 15.0000
    f[Q] = 1.0000
    f[VSS] = 25.0000
    f[PNOISE] = 0.0500
    f[ANOISE] = 0.0500
    f[CL_isv,V_isv,Q_isv,VSS_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 ],
    ]

