:orphan: 





.. _iv_two_cmp_vss_isv_fit:



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

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

:Diagram: 


.. thumbnail:: iv_two_cmp_vss_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          2.0209        1.0209         1.0209
f[V]                      15.0000          9.8915        5.1085         0.3406
f[Q]                       1.0000          1.0753        0.0753         0.0753
f[VSS]                    25.0000         38.3983       13.3983         0.5359
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.0500          0.0079        0.0421         0.8413
f[ANOISE]                  0.0500          0.0098        0.0402         0.8036
===============  ================  ==============  ============  =============

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


=================  ================  ==============  ============  =============
Variable Name        Starting Value    Fitted Value    Abs Change    Prop Change
=================  ================  ==============  ============  =============
f[CL_isv]                    0.2000          0.1000        0.1000         0.4999
f[CL_isv;V_isv]              0.0000          0.0390        0.0390       INF
f[CL_isv;Q_isv]              0.0000          0.0006        0.0006       INF
f[CL_isv;VSS_isv]            0.0000         -0.0263        0.0263       INF
f[V_isv;CL_isv]              0.0000          0.0390        0.0390       INF
f[V_isv]                     0.1000          0.1686        0.0686         0.6861
f[V_isv;Q_isv]               0.0000          0.0224        0.0224       INF
f[V_isv;VSS_isv]             0.0000          0.0106        0.0106       INF
f[Q_isv;CL_isv]              0.0000          0.0006        0.0006       INF
f[Q_isv;V_isv]               0.0000          0.0224        0.0224       INF
f[Q_isv]                     0.0500          0.0526        0.0026         0.0524
f[Q_isv;VSS_isv]             0.0000          0.0084        0.0084       INF
f[VSS_isv;CL_isv]            0.0000         -0.0263        0.0263       INF
f[VSS_isv;V_isv]             0.0000          0.0106        0.0106       INF
f[VSS_isv;Q_isv]             0.0000          0.0084        0.0084       INF
f[VSS_isv]                   0.2000          0.3335        0.1335         0.6677
=================  ================  ==============  ============  =============

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_vss_isv_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    -1710.7952


which required 1.30 iterations and took 166.55 seconds

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 ],
    ]



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


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

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

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

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

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

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

