Linear PD model

[Generated automatically as a Tutorial summary]

Model Description

Name:

linear_pd_pop

Title:

Linear PD model

Author:

PoPy for PK/PD

Abstract:

A simple (i.e. no PD compartments) Linear PD Model.
Model consists of a baseline which increases linearly with concentration.
Keywords:

pd; linear; one compartment model

Input Script:

linear_pd_tut.pyml

Diagram:

Comparison

True objective value

185.2839

Final fitted objective value

178.3795

Compare Main f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[BL]

15

9.86

10

1.37e-01

1.37%

f[SLOPE]

0.5

1.04

1

3.93e-02

3.93%

Compare Noise f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[PNOISE]

0.03

0.0148

0.01

4.81e-03

48.09%

f[ANOISE]

0.2

0.436

0.5

6.36e-02

12.72%

Compare Variance f[X]

Name

Initial

Fitted

True

Abs. Error

Prop. Error

f[BL_isv]

0.1

0.0537

0.05

3.72e-03

7.44%

f[BL_isv;SLOPE_isv]

0

0.00616

0

6.16e-03

inf

f[SLOPE_isv;BL_isv]

0

0.00616

0

6.16e-03

inf

f[SLOPE_isv]

0.05

0.0403

0.03

1.03e-02

34.20%

Outputs

Fitted f[X] values (after fitting)

f[BL] = 9.8634
f[SLOPE] = 1.0393
f[PNOISE] = 0.0148
f[ANOISE] = 0.4364
f[BL_isv,SLOPE_isv] = [
    [ 0.0537, 0.0062 ],
    [ 0.0062, 0.0403 ],
]

Generated data .csv file

Synthetic Data:

synthetic_data.csv

Gen and Fit Summaries

Inputs

True f[X] values (for simulation)

f[BL] = 10.0000
f[SLOPE] = 1.0000
f[PNOISE] = 0.0100
f[ANOISE] = 0.5000
f[BL_isv,SLOPE_isv] = [
    [ 0.0500, 0.0000 ],
    [ 0.0000, 0.0300 ],
]

Starting f[X] values (before fitting)

f[BL] = 15.0000
f[SLOPE] = 0.5000
f[PNOISE] = 0.0300
f[ANOISE] = 0.2000
f[BL_isv,SLOPE_isv] = [
    [ 0.1000, 0.0000 ],
    [ 0.0000, 0.0500 ],
]