:orphan: 





.. _dep_one_cmp_cl_isv_fit:



One Compartment Model with Absorption and Inter-subject Variance f[CL_isv]=0.2
##############################################################################

[Generated automatically as a Fitting summary]

Inputs
******



Description
===========

:Name: dep_one_cmp_cl_isv

:Title: One Compartment Model with Absorption and Inter-subject Variance f[CL_isv]=0.2

:Author: Wright Dose Ltd

:Abstract: 

| Population one Compartment Model with Absorption and Inter-subject Variance

:Keywords: one compartment model; dep_one_cmp_cl

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

:Input Data: :download:`synthetic_data.csv <synthetic_data.csv>`

:Diagram: 


.. thumbnail:: dep_one_cmp_cl_isv_fit.pyml_output/fit/compartment_diagram.svg
    :width: 200px


Initial fixed effect estimates
==============================

.. code-block:: pyml

    f[KA] = 0.5
    f[CL] = 1
    f[V] = 15
    f[PNOISE_STD] = 0.2
    f[ANOISE_STD] = 0.2
    f[CL_isv] = 0.01



Outputs
*******



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

.. code-block:: pyml

    -311.993580056


which required N. iterations and took 217.44 seconds

Final fitted fixed effects
==========================

.. code-block:: pyml

    f[KA] = 1
    f[CL] = 2.4482
    f[V] = 21.931
    f[PNOISE_STD] = 0.23417
    f[ANOISE_STD] = 0.037484
    f[CL_isv] = 0.12828



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


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

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

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

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

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



Plots
*****



Dense sim plots
===============



.. thumbnail:: images/fit_dense/000000.svg
    :width: 200px


.. thumbnail:: images/fit_dense/000001.svg
    :width: 200px


.. thumbnail:: images/fit_dense/000002.svg
    :width: 200px


Alternatively see :ref:`dep_one_cmp_cl_isv_dense_sim_plots`

Comparison
**********



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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[KA]                   1                     0.5       1              0.5
f[CL]                   2.44817               1         1.44817        1.44817
f[V]                   21.9308               15         0.462055       6.93082
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE_STD]         0.234172                0.2       0.17086       0.034172
f[ANOISE_STD]         0.0374836               0.2       0.812582      0.162516
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[CL_isv]              0.128278              0.01        11.8278      0.118278
===============  ==============  ================  =============  ============