:orphan: 





.. _indirect_pd_pop_fit:



Indirect_PKPD_model
###################

[Generated automatically as a Fitting summary]

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


:Name: indirect_pd_pop

:Title: Indirect_PKPD_model

:Author: PoPy for PK/PD

:Abstract: 

| A indirect (i.e. uses delay compartments) PD Model, based on the amount of drug in the body, delayed by two lag compartments
| The amount in the central compartment is determined by K, which has been estimated for each individual.
| The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

:Keywords: pd; one compartment model; indirect; delay compartment

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

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[BASE]                  500.0000        781.9491      281.9491         0.5639
f[KOUT]                    0.1000          0.0286        0.0714         0.7136
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.0500          0.0967        0.0467         0.9335
f[ANOISE]                  0.2000          0.2009        0.0009         0.0047
===============  ================  ==============  ============  =============

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


====================  ================  ==============  ============  =============
Variable Name           Starting Value    Fitted Value    Abs Change    Prop Change
====================  ================  ==============  ============  =============
f[BASE_isv]                     0.0200          0.0345        0.0145         0.7262
f[BASE_isv;KOUT_isv]            0.0000          0.0047        0.0047       INF
f[KOUT_isv;BASE_isv]            0.0000          0.0047        0.0047       INF
f[KOUT_isv]                     0.0200          0.0407        0.0207         1.0348
====================  ================  ==============  ============  =============

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:`indirect_pd_pop_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    3933.3780


which required 1.30 iterations and took 163.29 seconds

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


.. code-block:: pyml

    f[BASE] = 781.9491
    f[KOUT] = 0.0286
    f[PNOISE] = 0.0967
    f[ANOISE] = 0.2009
    f[BASE_isv,KOUT_isv] = [
        [ 0.0345, 0.0047 ],
        [ 0.0047, 0.0407 ],
    ]



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <indirect_pd_pop_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[BASE] = 500.0000
    f[KOUT] = 0.1000
    f[PNOISE] = 0.0500
    f[ANOISE] = 0.2000
    f[BASE_isv,KOUT_isv] = [
        [ 0.0200, 0.0000 ],
        [ 0.0000, 0.0200 ],
    ]

