:orphan: 





.. _iv_two_cmp_ab_isv_fit:



Population Two Compartment Model with AOB transformation and Inter-subject Variance
###################################################################################

[Generated automatically as a Fitting summary]

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


:Name: iv_two_cmp_ab_isv

:Title: Population Two Compartment Model with AOB transformation 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_ab; additive noise; AOB transform

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

:Diagram: 


.. thumbnail:: iv_two_cmp_ab_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[AOB]                     2.6180          3.2136        0.5956         0.2275
f[ALPHA]                   1.3090          0.2443        1.0647         0.8133
f[BETA]                    0.1910          0.0267        0.1643         0.8604
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE_STD]            100.0000          4.9974       95.0026         0.9500
===============  ================  ==============  ============  =============

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


=====================  ================  ==============  ============  =============
Variable Name            Starting Value    Fitted Value    Abs Change    Prop Change
=====================  ================  ==============  ============  =============
f[AOB_isv]                       2.6180          0.9699        1.6481         0.6295
f[AOB_isv;ALPHA_isv]             0.0000          0.0000        0.0000       INF
f[AOB_isv;BETA_isv]              0.0000          0.0000        0.0000       INF
f[ALPHA_isv;AOB_isv]             0.0000          0.0000        0.0000       INF
f[ALPHA_isv]                     0.2618          1.2487        0.9869         3.7696
f[ALPHA_isv;BETA_isv]            0.0000          0.0000        0.0000       INF
f[BETA_isv;AOB_isv]              0.0000          0.0000        0.0000       INF
f[BETA_isv;ALPHA_isv]            0.0000          0.0000        0.0000       INF
f[BETA_isv]                      0.0382          0.1810        0.1428         3.7389
=====================  ================  ==============  ============  =============

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_ab_isv_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    1848.3458


which required 1.29 iterations and took 100.89 seconds

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


.. code-block:: pyml

    f[AOB] = 3.2136
    f[ALPHA] = 0.2443
    f[BETA] = 0.0267
    f[AOB_isv,ALPHA_isv,BETA_isv] = [
        [ 0.9699, 0.0000, 0.0000 ],
        [ 0.0000, 1.2487, 0.0000 ],
        [ 0.0000, 0.0000, 0.1810 ],
    ]
    f[ANOISE_STD] = 4.9974



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <iv_two_cmp_ab_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[AOB] = 2.6180
    f[ALPHA] = 1.3090
    f[BETA] = 0.1910
    f[AOB_isv,ALPHA_isv,BETA_isv] = [
        [ 2.6180, 0.0000, 0.0000 ],
        [ 0.0000, 0.2618, 0.0000 ],
        [ 0.0000, 0.0000, 0.0382 ],
    ]
    f[ANOISE_STD] = 100.0000

