# Direct PD Model

[Generated automatically as a Tutorial summary]

## Inputs

### Description

Name: | direct_pd
Title: | Direct PD Model
Author: | Wright Dose Ltd
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: |

### True f[X] values

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

### Starting f[X] values

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

## Outputs

### Generating and Fitting Summaries

- Gen: Direct PD Model (gen)
- Fit: Direct PD Model (fit)

### Fitted f[X] values

    f[BASE] = 800.0443
    f[KOUT] = 0.0300
    f[ANOISE] = 10.0000

## Plots

### Dense comp plots

Alternatively see All dense_comp graph plots

## Comparison

### True objective value

    -52.6006

### Final fitted objective value

    460.7314

### Compare Main f[X]

Name | Initial | Fitted | True | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[BASE] | 500 | 800… | 800 | 0.01% | 4.43e-02
f[KOUT] | 0.1 | 0.03… | 0.03 | 0.02% | 5.70e-06

### Compare Noise f[X]

Name | Initial | Fitted | True | Prop. Error | Abs. Error
--- | --- | --- | --- | --- | ---
f[ANOISE] | 5 | 10 | 0.5 | 1900.00% | 9.50e+00

### Compare Variance f[X]

No Variance f[X] values to compare.