:orphan: 





.. _blq_pk_fit:



Depot + One compartment PK with BLQ
###################################

[Generated automatically as a Fitting summary]

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


:Name: blq_pk

:Title: Depot + One compartment PK with BLQ

:Author: PoPy for PK/PD

:Abstract: 

| Depot One Comp PK model, with BLQ (below level of quantification) observations.

:Keywords: tutorial; pk; advan4; dep_two_cmp; blq

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

:Diagram: 


.. thumbnail:: blq_pk_fit.pyml_output/compartment_diagram.svg
    :width: 200px


Comparison
**********



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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[KA]                      1.0000          0.2060        0.7940         0.7940
f[CL]                      1.0000          1.9987        0.9987         0.9987
f[V1]                     20.0000         50.9527       30.9527         1.5476
===============  ================  ==============  ============  =============

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


===============  ================  ==============  ============  =============
Variable Name      Starting Value    Fitted Value    Abs Change    Prop Change
===============  ================  ==============  ============  =============
f[PNOISE]                  0.1000          0.1469        0.0469         0.4694
===============  ================  ==============  ============  =============

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


================  ================  ==============  ============  =============
Variable Name       Starting Value    Fitted Value    Abs Change    Prop Change
================  ================  ==============  ============  =============
f[KA_isv]                   0.0500          0.0532        0.0032         0.0632
f[KA_isv;CL_isv]            0.0100          0.0111        0.0011         0.1131
f[KA_isv;V1_isv]            0.0100         -0.0286        0.0386         3.8623
f[CL_isv;KA_isv]            0.0100          0.0111        0.0011         0.1131
f[CL_isv]                   0.0500          0.0289        0.0211         0.4222
f[CL_isv;V1_isv]            0.0100          0.0239        0.0139         1.3938
f[V1_isv;KA_isv]            0.0100         -0.0286        0.0386         3.8623
f[V1_isv;CL_isv]            0.0100          0.0239        0.0139         1.3938
f[V1_isv]                   0.0500          0.0642        0.0142         0.2830
================  ================  ==============  ============  =============

Individual simulated (sim) plots
================================



.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000001.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000002.svg
    :width: 200px


.. thumbnail:: images/fit_sim_grph_outputs/indOBS_vs_TIME/000003.svg
    :width: 200px


Alternatively see :ref:`blq_pk_simulated_sim_plots`

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


(No population graphs were requested.)

Outputs
*******



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

.. code-block:: pyml

    -786.5417


which required 1.14 iterations and took 166.96 seconds

Fitted f[X] values (after fitting)
==================================


.. code-block:: pyml

    f[KA] = 0.2060
    f[CL] = 1.9987
    f[V1] = 50.9527
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0532, 0.0111, -0.0286 ],
        [ 0.0111, 0.0289, 0.0239 ],
        [ -0.0286, 0.0239, 0.0642 ],
    ]
    f[PNOISE] = 0.1469
    f[ANOISE] = 0.0100



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


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

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

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

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

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

:Likelihoods: :download:`lx_params.csv (fit) <blq_pk_fit.pyml_output/solN/lx_params.csv>`



Inputs
******


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


Starting f[X] values (before fitting)
=====================================


.. code-block:: pyml

    f[KA] = 1.0000
    f[CL] = 1.0000
    f[V1] = 20.0000
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0500, 0.0100, 0.0100 ],
        [ 0.0100, 0.0500, 0.0100 ],
        [ 0.0100, 0.0100, 0.0500 ],
    ]
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0100

