# Simple Tut Example

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | tut_example1
Title: | Simple Tut Example
Author: | J.R. Hartley
Abstract: |

One compartment model with elimination rate constant KE.

Keywords: | one compartment model; iv_one_cmp_k
Input Script: | `tut_example1_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    f[KE] = 0.0500
    f[PNOISE] = 0.1000
    f[KE_isv] = 0.1000

## Outputs

### Final objective value

    -48.4358

which required 9 iterations and took 6.13 seconds

### Final fitted fixed effects

    f[KE] = 0.1062
    f[PNOISE] = 0.0450
    f[KE_isv] = 0.0274

### 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[KE] | 0.1062 | 0.0500 | 1.1234 | 0.0562

### Compare Noise f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.0450 | 0.1000 | 0.5502 | 0.0550

### Compare Variance f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KE_isv] | 0.0274 | 0.1000 | 0.7261 | 0.0726