:orphan: 





.. _flip_flop_bad_pop_gen:



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

[Generated automatically as a Generation summary]

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


:Name: flip_flop_bad_pop

: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.
| 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 bad fit in the false minima.

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

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

:Diagram: 


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


Outputs
*******



Individual simulated (sim) plots
================================



.. thumbnail:: images/gen_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/gen_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/gen_sim_grph_outputs/indOBS_vs_TIME/000003.svg
    :width: 200px


Alternatively see :ref:`flip_flop_bad_pop_simulated_sim_plots`

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


(No population graphs were requested.)

Generated parameter .csv files
==============================


:Fixed Effects: :download:`fx_params.csv (gen) <flip_flop_bad_pop_gen.pyml_output/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (gen) <flip_flop_bad_pop_gen.pyml_output/rx_params.csv>`

:Model params: :download:`mx_params.csv (gen) <flip_flop_bad_pop_gen.pyml_output/mx_params.csv>`

:State values: :download:`sx_params.csv (gen) <flip_flop_bad_pop_gen.pyml_output/sx_params.csv>`

:Predictions: :download:`px_params.csv (gen) <flip_flop_bad_pop_gen.pyml_output/px_params.csv>`


:Observations: :download:`synthetic_data.csv (gen) <synthetic_data.csv>`


Inputs
******



True f[X] values (for simulation)
=================================


.. code-block:: pyml

    f[KE] = 0.0500
    f[V] = 40.0000
    f[KA] = 0.6000
    f[PNOISE] = 0.0100
    f[ANOISE] = 0.0500
    f[KE_isv,V_isv,KA_isv] = [
        [ 0.0500, 0.0000, 0.0000 ],
        [ 0.0000, 0.0500, 0.0000 ],
        [ 0.0000, 0.0000, 0.0500 ],
    ]

