:orphan: 





.. _dep_one_cmp_cl_iov_naive_fit:



One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0
################################################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: dep_one_cmp_cl_iov_naive

:Title: One Compartment Model with Absorption and no inter-occasion Variance f[CL_iov]=0

:Author: Wright Dose Ltd

:Abstract: 

| Population one Compartment Model with Absorption and Inter-occasion Variance
| Here f[CL_iov] is not estimated it is set to zero.

:Keywords: one compartment model; dep_one_cmp_cl; iov

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

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

:Diagram: 


.. thumbnail:: dep_one_cmp_cl_iov_naive_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
    f[CL_iov] = 0



Outputs
*******



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

.. code-block:: pyml

    -163.411429465


which required 15 iterations and took 82.37 seconds

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

.. code-block:: pyml

    f[KA] = 1
    f[CL] = 2.1462
    f[V] = 22.086
    f[PNOISE_STD] = 0.46359
    f[ANOISE_STD] = 0.061732
    f[CL_isv] = 4.2967
    f[CL_iov] = 0



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


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

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

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

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

:Predictions: :download:`px_params.csv (fit) <dep_one_cmp_cl_iov_naive_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_iov_naive_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]                   2.14619               1         1.14619        1.14619
f[V]                   22.0861               15         0.472407       7.08611
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE_STD]         0.463588                0.2       1.31794       0.263588
f[ANOISE_STD]         0.0617322               0.2       0.691339      0.138268
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[CL_isv]               4.29671              0.01        428.671       4.28671
===============  ==============  ================  =============  ============