# Simple Tut Example

[Generated automatically as a Fitting summary]

## Model Description

Name:

  tut_example1

Title:

  Simple Tut Example

Author:

  PoPy for PK/PD

Abstract:

One compartment model with elimination rate constant KE.

Keywords:

  one compartment model; iv_one_cmp_k

Input Script:

  `tut_example1_fit.pyml`

Diagram:

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KE] | 0.0500 | 0.1062 | 0.0562 | 1.1233

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1000 | 0.0450 | 0.0550 | 0.5503

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KE_isv] | 0.1000 | 0.0260 | 0.0740 | 0.7398

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

| allOBS(DV_CENTRAL)_vs_IPRED(CEN)
| allOBS(DV_CENTRAL)_vs_PPRED(CEN)
| allOBS_vs_TIME
| CWRES(DV_CENTRAL)_wrt_IPRED(CEN)
| RES(DV_CENTRAL)_wrt_IPRED(CEN)
| WRES(DV_CENTRAL)_wrt_IPRED(CEN)
| WRES(DV_CENTRAL)_wrt_PPRED(CEN)

## Outputs

### Final objective value

    -48.4038

which required 1.11 iterations and took 34.89 seconds

### Fitted f[X] values (after fitting)

    f[KE] = 0.1062
    f[PNOISE] = 0.0450
    f[KE_isv] = 0.0260

### 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)`

Likelihoods:

  `lx_params.csv (fit)`

## Inputs

Input Data:

  `cx_obs_params.csv`

### Starting f[X] values (before fitting)

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