# Direct PD Model

[Generated automatically as a Tutorial summary]

## Model Description

Name:

  direct_pd

Title:

  Direct PD Model

Author:

  PoPy for PK/PD

Abstract:

A simple direct PD Model, based on the amount of drug in the body.
The amount in the central compartment is determined by K, which has been previously estimated for each individual.
The amount in the central compartment influences the rate of removal of a biomarker (KOUT).

Keywords:

  pd; one compartment model; direct

Input Script:

  `direct_pd_tut.pyml`

Diagram:

## Comparison

### True objective value

    -52.6006

### Final fitted objective value

    460.5766

### Compare Main f[X]

Name | Initial | Fitted | True | Abs. Error | Prop. Error
--- | --- | --- | --- | --- | ---
f[BASE] | 500 | 800… | 800 | 4.99e-02 | 0.01%
f[KOUT] | 0.1 | 0.03… | 0.03 | 5.52e-06 | 0.02%

### Compare Noise f[X]

Name | Initial | Fitted | True | Abs. Error | Prop. Error
--- | --- | --- | --- | --- | ---
f[ANOISE] | 5 | 9.99 | 0.5 | 9.49e+00 | 1898.45%

### Compare Variance f[X]

No Variance f[X] values to compare.

## Outputs

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

    f[BASE] = 800.0499
    f[KOUT] = 0.0300
    f[ANOISE] = 9.9922

### Generated data .csv file

Synthetic Data:

  `synthetic_data.csv`

### Gen and Fit Summaries

- Gen: Direct PD Model (gen)

- Fit: Direct PD Model (fit)

## Inputs

### True f[X] values (for simulation)

    f[BASE] = 800.0000
    f[KOUT] = 0.0300
    f[ANOISE] = 0.5000

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

    f[BASE] = 500.0000
    f[KOUT] = 0.1000
    f[ANOISE] = 5.0000