:orphan: 





.. _flip_flop_good_pop_fit:



Flip Flop tutorial with low initial estimates of KA, V and CL
#############################################################

[Generated automatically as a Fitting summary]

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


:Name: flip_flop_good_pop

:Title: Flip Flop tutorial with low initial estimates of KA, V and CL

:Author: PoPy for PK/PD

:Abstract: 

| One compartment model with a depot leading into a central compartment.
| Note another interpretation of this is that the model is just over parametrised. Because here
| the KE and V parameters act independently unlike the CL/V and V parametrisation.
| Here initial values of KE, KA, V are close to the flipped values, so we get a good fit near the true global minima.

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

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

:Diagram: 


.. thumbnail:: flip_flop_good_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[KE]                      0.0200          0.0494        0.0294         1.4692
f[V]                      30.0000         39.4926        9.4926         0.3164
f[KA]                      0.3000          0.5716        0.2716         0.9055
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.0500          0.0069        0.0431         0.8614
f[ANOISE]                  0.1000          0.0536        0.0464         0.4644
===============  ================  ==============  ============  =============

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


================  ================  ==============  ============  =============
Variable Name       Starting Value    Fitted Value    Abs Change    Prop Change
================  ================  ==============  ============  =============
f[KE_isv]                   0.0200          0.0439        0.0239         1.1967
f[KE_isv;V_isv]             0.0000         -0.0084        0.0084       INF
f[KE_isv;KA_isv]            0.0000          0.0091        0.0091       INF
f[V_isv;KE_isv]             0.0000         -0.0084        0.0084       INF
f[V_isv]                    0.0200          0.0374        0.0174         0.8705
f[V_isv;KA_isv]             0.0000          0.0168        0.0168       INF
f[KA_isv;KE_isv]            0.0000          0.0091        0.0091       INF
f[KA_isv;V_isv]             0.0000          0.0168        0.0168       INF
f[KA_isv]                   0.0200          0.0439        0.0239         1.1944
================  ================  ==============  ============  =============

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:`flip_flop_good_pop_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    -282.2522


which required 1.28 iterations and took 153.32 seconds

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


.. code-block:: pyml

    f[KE] = 0.0494
    f[V] = 39.4926
    f[KA] = 0.5716
    f[PNOISE] = 0.0069
    f[ANOISE] = 0.0536
    f[KE_isv,V_isv,KA_isv] = [
        [ 0.0439, -0.0084, 0.0091 ],
        [ -0.0084, 0.0374, 0.0168 ],
        [ 0.0091, 0.0168, 0.0439 ],
    ]



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <flip_flop_good_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[KE] = 0.0200
    f[V] = 30.0000
    f[KA] = 0.3000
    f[PNOISE] = 0.0500
    f[ANOISE] = 0.1000
    f[KE_isv,V_isv,KA_isv] = [
        [ 0.0200, 0.0000, 0.0000 ],
        [ 0.0000, 0.0200, 0.0000 ],
        [ 0.0000, 0.0000, 0.0200 ],
    ]

