:orphan: 





.. _iv_two_cmp_isv_fit:



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

[Generated automatically as a Fitting 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_fit.pyml <iv_two_cmp_isv_fit.pyml>`

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[K12]                     0.5000          0.1893        0.3107         0.6214
f[K21]                     0.5000          0.1229        0.3771         0.7542
f[KE]                      0.5000          0.1034        0.3966         0.7931
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE_STD]            100.0000          4.9307       95.0693         0.9507
===============  ================  ==============  ============  =============

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


==================  ================  ==============  ============  =============
Variable Name         Starting Value    Fitted Value    Abs Change    Prop Change
==================  ================  ==============  ============  =============
f[KE_isv]                     0.0100          0.2973        0.2873        28.7261
f[KE_isv;K12_isv]             0.0000          0.0000        0.0000       INF
f[KE_isv;K21_isv]             0.0000          0.0000        0.0000       INF
f[K12_isv;KE_isv]             0.0000          0.0000        0.0000       INF
f[K12_isv]                    0.0100          0.1608        0.1508        15.0767
f[K12_isv;K21_isv]            0.0000          0.0000        0.0000       INF
f[K21_isv;KE_isv]             0.0000          0.0000        0.0000       INF
f[K21_isv;K12_isv]            0.0000          0.0000        0.0000       INF
f[K21_isv]                    0.0100          0.1855        0.1755        17.5462
==================  ================  ==============  ============  =============

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



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


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


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


Alternatively see :ref:`iv_two_cmp_isv_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    1795.0662


which required 1.26 iterations and took 152.40 seconds

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



Fitted parameter .csv files
===========================


:Fixed Effects: :download:`fx_params.csv (fit) <iv_two_cmp_isv_fit.pyml_output/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <iv_two_cmp_isv_fit.pyml_output/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <iv_two_cmp_isv_fit.pyml_output/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <iv_two_cmp_isv_fit.pyml_output/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <iv_two_cmp_isv_fit.pyml_output/solN/px_params.csv>`

:Likelihoods: :download:`lx_params.csv (fit) <iv_two_cmp_isv_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


:Input Data: :download:`cx_obs_params.csv <cx_obs_params.csv>`


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

