# Simple Fit Example

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | fit_example1
Title: | Simple Fit Example
Author: | J.R. Hartley
Abstract: |

One compartment model with elimination rate constant KE.

Keywords: | one compartment model; iv_one_cmp_k
Input Script: | `fit_example1.pyml`
Input Data: | `fit_example1_data.csv`
Diagram: |

### Initial fixed effect estimates

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

## Outputs

### Final objective value

    303.0548

which required 1.13 iterations and took 84.83 seconds

### Final fitted fixed effects

    f[KE] = 0.0968
    f[PNOISE] = 0.2136
    f[KE_isv] = 0.0160

### 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 | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KE] | 0.0500 | 0.0968 | 0.9356 | 0.0468

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1000 | 0.2136 | 1.1355 | 0.1136

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KE_isv] | 0.1000 | 0.0160 | 0.8398 | 0.0840