:orphan: 





.. _flip_flop_bad_fit:



Flip Flop tutorial with inaccurate starting values
##################################################

[Generated automatically as a Fitting summary]

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


:Name: flip_flop_bad

:Title: Flip Flop tutorial with inaccurate starting values

:Author: PoPy for PK/PD

:Abstract: 

| One compartment model with a depot leading into a central compartment.
| Here initial values of KE, KA, V are close to the flipped values, so we get a bad fit in the false minima.

:Keywords: flip flop; dep_one_cmp_k; one compartment model; poor start values

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

:Diagram: 


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


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[KA]                      0.1000          0.1526        0.0526         0.5258
f[KE]                      1.0000          0.3014        0.6986         0.6986
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[ANOISE_STD]              5.0000          0.9207        4.0793         0.8159
===============  ================  ==============  ============  =============

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




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

    83.4768


which required 1.17 iterations and took 11.11 seconds

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


.. code-block:: pyml

    f[KA] = 0.1526
    f[KE] = 0.3014
    f[ANOISE_STD] = 0.9207



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <flip_flop_bad_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[KA] = 0.1000
    f[KE] = 1.0000
    f[ANOISE_STD] = 5.0000

