:orphan: 





.. _dep_one_cmp_cl_iov_05_fit:



One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5
###############################################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: dep_one_cmp_cl_iov_05

:Title: One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.5

:Author: Wright Dose Ltd

:Abstract: 

| Population one Compartment Model with Absorption and Inter-occasion Variance
| Here f[CL_isv] true value is 0.5

:Keywords: one compartment model; dep_one_cmp_cl; iov

:Input Script: :download:`dep_one_cmp_cl_iov_05_fit.pyml <dep_one_cmp_cl_iov_05_fit.pyml>`

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

:Diagram: 


.. thumbnail:: dep_one_cmp_cl_iov_05_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] = 0.01



Outputs
*******



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

.. code-block:: pyml

    -276.596757751


which required 24 iterations and took 130.74 seconds

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

.. code-block:: pyml

    f[KA] = 1
    f[CL] = 1.8513
    f[V] = 20.269
    f[PNOISE_STD] = 0.21367
    f[ANOISE_STD] = 0.047704
    f[CL_isv] = 0.26995
    f[CL_iov] = 0.0085567



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


:Fixed Effects: :download:`fx_params.csv (fit) <dep_one_cmp_cl_iov_05_fit.pyml_output/fit/solN/fx_params.csv>`

:Random Effects: :download:`rx_params.csv (fit) <dep_one_cmp_cl_iov_05_fit.pyml_output/fit/solN/rx_params.csv>`

:Model params: :download:`mx_params.csv (fit) <dep_one_cmp_cl_iov_05_fit.pyml_output/fit/solN/mx_params.csv>`

:State values: :download:`sx_params.csv (fit) <dep_one_cmp_cl_iov_05_fit.pyml_output/fit/solN/sx_params.csv>`

:Predictions: :download:`px_params.csv (fit) <dep_one_cmp_cl_iov_05_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_05_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]                   1.85134               1         0.851343      0.851343
f[V]                   20.2693               15         0.351284      5.26926
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE_STD]         0.213669                0.2      0.0683461     0.0136692
f[ANOISE_STD]         0.0477045               0.2      0.761478      0.152296
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[CL_isv]            0.269953                0.01      25.9953      0.259953
f[CL_iov]            0.00855672              0.01       0.144328    0.00144328
===============  ==============  ================  =============  ============