:orphan: 





.. _builtin_fit_example_fit:



First order absorption model with peripheral compartment
########################################################

[Generated automatically as a Fitting summary]

Inputs
******



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

:Name: builtin_fit_example

:Title: First order absorption model with peripheral compartment

:Author: J.R. Hartley

:Abstract: 

| A two compartment PK model with bolus dose and
| first order absorption, similar to a Nonmem advan4trans4 model.

:Keywords: fitting; pk; advan4; dep_two_cmp; first order

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

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

:Diagram: 


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


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

.. code-block:: pyml

    f[KA] = 1
    f[CL] = 1
    f[V1] = 20
    f[Q] = 0.5
    f[V2] = 100
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.05, 0.01, 0.01, 0.01, 0.01 ],
        [ 0.01, 0.05, 0.01, 0.01, 0.01 ],
        [ 0.01, 0.01, 0.05, 0.01, 0.01 ],
        [ 0.01, 0.01, 0.01, 0.05, 0.01 ],
        [ 0.01, 0.01, 0.01, 0.01, 0.05 ]
    ]
    f[PNOISE] = 0.1



Outputs
*******



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

.. code-block:: pyml

    -896.875222683


which required 33 iterations and took 208.05 seconds

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

.. code-block:: pyml

    f[KA] = 0.22502
    f[CL] = 2.0883
    f[V1] = 54.663
    f[Q] = 0.94563
    f[V2] = 105.35
    f[KA_isv,CL_isv,V1_isv,Q_isv,V2_isv] = [
        [ 0.14689, 0.013803, -0.056237, 0.101, -0.011802 ],
        [ 0.013803, 0.033066, 0.0062645, -0.0050783, 0.00014454 ],
        [ -0.056237, 0.0062645, 0.043295, -0.047294, 0.014371 ],
        [ 0.101, -0.0050783, -0.047294, 0.23317, -0.033465 ],
        [ -0.011802, 0.00014454, 0.014371, -0.033465, 0.05129 ]
    ]
    f[PNOISE] = 0.14293



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


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

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

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

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

:Predictions: :download:`px_params.csv (fit) <builtin_fit_example.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:`builtin_fit_example_dense_sim_plots`

Comparison
**********



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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[KA]                  0.225015               1        0.774985       0.774985
f[CL]                  2.08828                1        1.08828        1.08828
f[V1]                 54.6634                20        1.73317       34.6634
f[Q]                   0.945631               0.5      0.891262       0.445631
f[V2]                105.351                100        0.0535103      5.35103
===============  ==============  ================  =============  ============

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


===============  ==============  ================  =============  ============
Variable Name      Fitted Value    Starting Value    Prop Change    Abs Change
===============  ==============  ================  =============  ============
f[PNOISE]               0.14293               0.1       0.429302     0.0429302
===============  ==============  ================  =============  ============

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


================  ==============  ================  =============  ============
Variable Name       Fitted Value    Starting Value    Prop Change    Abs Change
================  ==============  ================  =============  ============
f[KA_isv]            0.146886                 0.05      1.93773      0.0968864
f[KA_isv;CL_isv]     0.0138028                0.01      0.380285     0.00380285
f[KA_isv;V1_isv]    -0.056237                 0.01      6.6237       0.066237
f[KA_isv;Q_isv]      0.100996                 0.01      9.09964      0.0909964
f[KA_isv;V2_isv]    -0.0118023                0.01      2.18023      0.0218023
f[CL_isv;KA_isv]     0.0138028                0.01      0.380285     0.00380285
f[CL_isv]            0.0330657                0.05      0.338686     0.0169343
f[CL_isv;V1_isv]     0.00626453               0.01      0.373547     0.00373547
f[CL_isv;Q_isv]     -0.00507828               0.01      1.50783      0.0150783
f[CL_isv;V2_isv]     0.000144541              0.01      0.985546     0.00985546
f[V1_isv;KA_isv]    -0.056237                 0.01      6.6237       0.066237
f[V1_isv;CL_isv]     0.00626453               0.01      0.373547     0.00373547
f[V1_isv]            0.0432946                0.05      0.134108     0.00670542
f[V1_isv;Q_isv]     -0.047294                 0.01      5.7294       0.057294
f[V1_isv;V2_isv]     0.0143706                0.01      0.437063     0.00437063
f[Q_isv;KA_isv]      0.100996                 0.01      9.09964      0.0909964
f[Q_isv;CL_isv]     -0.00507828               0.01      1.50783      0.0150783
f[Q_isv;V1_isv]     -0.047294                 0.01      5.7294       0.057294
f[Q_isv]             0.233175                 0.05      3.6635       0.183175
f[Q_isv;V2_isv]     -0.033465                 0.01      4.3465       0.043465
f[V2_isv;KA_isv]    -0.0118023                0.01      2.18023      0.0218023
f[V2_isv;CL_isv]     0.000144541              0.01      0.985546     0.00985546
f[V2_isv;V1_isv]     0.0143706                0.01      0.437063     0.00437063
f[V2_isv;Q_isv]     -0.033465                 0.01      4.3465       0.043465
f[V2_isv]            0.05129                  0.05      0.0258006    0.00129003
================  ==============  ================  =============  ============