# One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | d1cmp_cl_iov
Title: | One Compartment Model with Absorption and Inter-occasion Variance f[CL_isv]=0.2
Author: | Wright Dose Ltd
Abstract: |

Population one Compartment Model with Absorption and Inter-occasion Variance

Keywords: | one compartment model; dep_one_cmp_cl; iov
Input Script: | `d1cmp_cl_iov_fit.pyml`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    f[KA] = 0.5000
    f[CL] = 1.0000
    f[V] = 15.0000
    f[PNOISE_STD] = 0.2000
    f[ANOISE_STD] = 0.2000
    f[CL_isv] = 0.0100
    f[CL_iov] = 0.0100

## Outputs

### Final objective value

    -363.8118

which required 1.19 iterations and took 303.58 seconds

### Final fitted fixed effects

    f[KA] = 0.3345
    f[CL] = 2.5856
    f[V] = 20.2051
    f[PNOISE_STD] = 0.1000
    f[ANOISE_STD] = 0.0479
    f[CL_isv] = 0.1199
    f[CL_iov] = 0.0783

### 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[KA] | 0.5000 | 0.3345 | 0.3309 | 0.1655
f[CL] | 1.0000 | 2.5856 | 1.5856 | 1.5856
f[V] | 15.0000 | 20.2051 | 0.3470 | 5.2051

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE_STD] | 0.2000 | 0.1000 | 0.4998 | 0.1000
f[ANOISE_STD] | 0.2000 | 0.0479 | 0.7605 | 0.1521

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[CL_isv] | 0.0100 | 0.1199 | 10.9948 | 0.1099
f[CL_iov] | 0.0100 | 0.0783 | 6.8312 | 0.0683