:orphan: 





.. _pa_gen_po_fit_fit:



Proportional error model fitted to proportional + additive noise synthetic data.
################################################################################

[Generated automatically as a Fitting summary]

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


:Name: pa_gen_po_fit

:Title: Proportional error model fitted to proportional + additive noise synthetic data.

:Author: PoPy for PK/PD

:Abstract: 

| One compartment model with a depot leading to a central compartment
| This model contains both proportional error only. Input data contains proportional and additive noise.

:Keywords: one compartment model; dep_one_cmp_cl; proportional error

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

:Diagram: 


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


Comparison
**********



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




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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE_STD]              0.5000          0.5223        0.0223         0.0446
===============  ================  ==============  ============  =============

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




Population observed (fit) plots
===============================


.. list-table:: 
    :width: 90%

    * - .. thumbnail:: images/fit_grph_outputs/indOBS_vs_TIME/000001.svg
            :width: 200px
      - indOBS_vs_TIME

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


.. list-table:: 
    :width: 90%

    * - .. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
            :width: 200px
      - indOBS_vs_TIME

Outputs
*******



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

.. code-block:: pyml

    -228.0882


which required 1.4 iterations and took 10.42 seconds

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


.. code-block:: pyml

    f[PNOISE_STD] = 0.5223



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <pa_gen_po_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


:Input Data: :download:`synthetic_data.csv <synthetic_data.csv>`


Starting f[X] values (before fitting)
=====================================


.. code-block:: pyml

    f[PNOISE_STD] = 0.5000

