:orphan: 





.. _iv_two_cmp_cl_isv_fit:



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

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

:Diagram: 


.. thumbnail:: iv_two_cmp_cl_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[CL]                      1.0000          2.3258        1.3258         1.3258
f[V1]                     15.0000          7.3693        7.6307         0.5087
f[Q]                       1.0000          1.0653        0.0653         0.0653
f[V2]                     25.0000         10.1994       14.8006         0.5920
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.0500          0.0123        0.0377         0.7537
f[ANOISE]                  0.0500          0.0093        0.0407         0.8149
===============  ================  ==============  ============  =============

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


================  ================  ==============  ============  =============
Variable Name       Starting Value    Fitted Value    Abs Change    Prop Change
================  ================  ==============  ============  =============
f[CL_isv]                   0.2000          0.1914        0.0086         0.0430
f[CL_isv;V1_isv]            0.0000          0.2094        0.2094       INF
f[CL_isv;Q_isv]             0.0000         -0.0294        0.0294       INF
f[CL_isv;V2_isv]            0.0000         -0.0940        0.0940       INF
f[V1_isv;CL_isv]            0.0000          0.2094        0.2094       INF
f[V1_isv]                   0.1000          0.6863        0.5863         5.8627
f[V1_isv;Q_isv]             0.0000         -0.0546        0.0546       INF
f[V1_isv;V2_isv]            0.0000          0.1520        0.1520       INF
f[Q_isv;CL_isv]             0.0000         -0.0294        0.0294       INF
f[Q_isv;V1_isv]             0.0000         -0.0546        0.0546       INF
f[Q_isv]                    0.0500          0.1462        0.0962         1.9239
f[Q_isv;V2_isv]             0.0000          0.0294        0.0294       INF
f[V2_isv;CL_isv]            0.0000         -0.0940        0.0940       INF
f[V2_isv;V1_isv]            0.0000          0.1520        0.1520       INF
f[V2_isv;Q_isv]             0.0000          0.0294        0.0294       INF
f[V2_isv]                   0.2000          0.6832        0.4832         2.4161
================  ================  ==============  ============  =============

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_cl_isv_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    -1654.2493


which required 1.30 iterations and took 153.96 seconds

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 ],
    ]



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <iv_two_cmp_cl_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[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 ],
    ]

