# Diagonal matrix generation diagonal matrix fit using separate univariate normals

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | gen_indep_fit_indep
Title: | Diagonal matrix generation diagonal matrix fit using separate univariate normals
Author: | Wright Dose Ltd
Abstract: |

One compartment model with absorption compartment and CL/V parametrisation.
This script uses a diagonal covariance matrix to generate the data and a diagonal covariance matrix to fit.
Note here the ‘diagonal matrix’ is implemented as two separate univariate normal distributions, which is equivalent.

Keywords: | dep_one_cmp_cl; one compartment model; diagonal matrix
Input Script: | `gen_indep_fit_indep_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500
    f[CL_isv] = 0.0100
    f[V_isv] = 0.0100

## Outputs

### Final objective value

    -2172.8213

which required N. iterations and took 477.73 seconds

### Final fitted fixed effects

    f[KA] = 0.3000
    f[CL] = 3.0000
    f[V] = 20.0000
    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0500
    f[CL_isv] = 0.1763
    f[V_isv] = 0.0862

### 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]

### Compare Noise f[X]

### Compare Variance f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[CL_isv] | 0.1763 | 0.0100 | 16.6342 | 0.1663
f[V_isv] | 0.0862 | 0.0100 | 7.6249 | 0.0762