:orphan: 





.. _linear_pd_pop_gen:



Linear PD model
###############

[Generated automatically as a Generation summary]

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


:Name: linear_pd_pop

:Title: Linear PD model

:Author: PoPy for PK/PD

:Abstract: 

| A simple (i.e. no PD compartments) Linear PD Model.
| Model consists of a baseline which increases linearly with concentration.

:Keywords: pd; linear; one compartment model

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

:Diagram: 


.. thumbnail:: linear_pd_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:`linear_pd_pop_simulated_sim_plots`

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


(No population graphs were requested.)

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


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

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

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

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

:Predictions: :download:`px_params.csv (gen) <linear_pd_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[BL] = 10.0000
    f[SLOPE] = 1.0000
    f[PNOISE] = 0.0100
    f[ANOISE] = 0.5000
    f[BL_isv,SLOPE_isv] = [
        [ 0.0500, 0.0000 ],
        [ 0.0000, 0.0300 ],
    ]

