:orphan: 





.. _iv_two_cmp_ab_isv_gen:



Population Two Compartment Model with AOB transformation and Inter-subject Variance
###################################################################################

[Generated automatically as a Generation summary]

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


:Name: iv_two_cmp_ab_isv

:Title: Population Two Compartment Model with AOB transformation and Inter-subject Variance

:Author: PoPy for PK/PD

:Abstract: 

| Population One Compartment Model and Inter-subject Variance

:Keywords: two compartment model; iv_two_cmp_ab; additive noise; AOB transform

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

:Diagram: 


.. thumbnail:: iv_two_cmp_ab_isv_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:`iv_two_cmp_ab_isv_simulated_sim_plots`

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


(No population graphs were requested.)

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


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

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

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

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

:Predictions: :download:`px_params.csv (gen) <iv_two_cmp_ab_isv_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[AOB] = 2.3187
    f[ALPHA] = 0.4137
    f[BETA] = 0.0363
    f[AOB_isv,ALPHA_isv,BETA_isv] = [
        [ 2.6180, 0.0000, 0.0000 ],
        [ 0.0000, 1.1708, 0.0000 ],
        [ 0.0000, 0.0000, 0.1708 ],
    ]
    f[ANOISE_STD] = 5.0000

