:orphan: 





.. _iv_one_cmp_isv_fit:



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

[Generated automatically as a Fitting summary]

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


:Name: iv_one_cmp_isv

:Title: Population One Compartment Model and Inter-subject Variance

:Author: PoPy for PK/PD

:Abstract: 

| Population One Compartment Model and Inter-subject Variance

:Keywords: one compartment model; iv_one_cmp_k; additive noise

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

:Diagram: 


.. thumbnail:: iv_one_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[KE]                      0.5000          0.0753        0.4247         0.8495
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE_STD]            100.0000          4.9669       95.0331         0.9503
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[KE_isv]                  0.0100          0.2379        0.2279        22.7917
===============  ================  ==============  ============  =============

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_one_cmp_isv_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    1780.5526


which required 1.16 iterations and took 47.84 seconds

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


.. code-block:: pyml

    f[KE] = 0.0753
    f[KE_isv] = 0.2379
    f[ANOISE_STD] = 4.9669



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <iv_one_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[KE] = 0.5000
    f[KE_isv] = 0.0100
    f[ANOISE_STD] = 100.0000

