# Sine circadian model

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | circ_sin
Title: | Sine circadian model
Author: | Wright Dose Ltd
Abstract: |

A PD Model based on the amount of drug in the body.
The PD model uses a sine function which simulates a circadian rhythm for the generation of a biomarker.
The amount in the central compartment is determined by CL and V, PK patameters , which have been previosly estimated for each individual.
The amount in the central compartment influences the rate of production of a biomarker.

Keywords: | PD; Pharmacodynamics; sine function; Circadian rhythm
Input Script: | `circ_sin_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    f[AMP] = 3.0000
    f[INT] = 16.0000
    f[KOUT] = 0.1000
    f[ANOISE] = 5.0000

## Outputs

### Final objective value

    1174.8667

which required N. iterations and took 2.52 seconds

### Final fitted fixed effects

    f[AMP] = 2.0061
    f[INT] = 19.8394
    f[KOUT] = 0.0501
    f[ANOISE] = 10.0000

### Fitted parameter .csv files

Fixed Effects: | `fx_params.csv (fit)`
Random Effects: | `rx_params.csv (fit)`
Model params: | `mx_params.csv (fit)`
State values: | `sx_params.csv (fit)`
Predictions: | `px_params.csv (fit)`

## Plots

### Dense sim plots

Alternatively see All dense_sim graph plots

## Comparison

### Compare Main f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[AMP] | 2.0061 | 3.0000 | 0.3313 | 0.9939
f[INT] | 19.8394 | 16.0000 | 0.2400 | 3.8394
f[KOUT] | 0.0501 | 0.1000 | 0.4988 | 0.0499

### Compare Noise f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[ANOISE] | 10.0000 | 5.0000 | 1.0000 | 5.0000

### Compare Variance f[X]