# Depot One Comp PK with BLQ observations set to LLQ

[Generated automatically as a Fitting summary]

## Model Description

Name:

  blq_pk_norm_fit

Title:

  Depot One Comp PK with BLQ observations set to LLQ

Author:

  PoPy for PK/PD

Abstract:

Depot One Comp PK model, with BLQ (below level of quantification)
observations set to LLQ (lower limit of quantification).

Keywords:

  tutorial; pk; advan4; dep_two_cmp; blq

Input Script:

  `blq_pk_norm_fit.pyml`

Diagram:

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA] | 1.0000 | 5.8108 | 4.8108 | 4.8108
f[CL] | 1.0000 | 0.9490 | 0.0510 | 0.0510
f[V1] | 20.0000 | 88.6022 | 68.6022 | 3.4301

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1000 | 0.2207 | 0.1207 | 1.2071

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA_isv] | 0.0500 | 1.3304 | 1.2804 | 25.6080
f[KA_isv;CL_isv] | 0.0100 | -0.0176 | 0.0276 | 2.7570
f[KA_isv;V1_isv] | 0.0100 | 0.2359 | 0.2259 | 22.5922
f[CL_isv;KA_isv] | 0.0100 | -0.0176 | 0.0276 | 2.7570
f[CL_isv] | 0.0500 | 0.0002 | 0.0498 | 0.9953
f[CL_isv;V1_isv] | 0.0100 | -0.0031 | 0.0131 | 1.3114
f[V1_isv;KA_isv] | 0.0100 | 0.2359 | 0.2259 | 22.5922
f[V1_isv;CL_isv] | 0.0100 | -0.0031 | 0.0131 | 1.3114
f[V1_isv] | 0.0500 | 0.0444 | 0.0056 | 0.1124

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

(No population graphs were requested.)

## Outputs

### Final objective value

    121897.7219

which required 1.30 iterations and took 74.11 seconds

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

    f[KA] = 5.8108
    f[CL] = 0.9490
    f[V1] = 88.6022
    f[KA_isv,CL_isv,V1_isv] = [
        [ 1.3304, -0.0176, 0.2359 ],
        [ -0.0176, 0.0002, -0.0031 ],
        [ 0.2359, -0.0031, 0.0444 ],
    ]
    f[PNOISE] = 0.2207
    f[ANOISE] = 0.0100

### 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:

  `synthetic_data.csv`

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

    f[KA] = 1.0000
    f[CL] = 1.0000
    f[V1] = 20.0000
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0500, 0.0100, 0.0100 ],
        [ 0.0100, 0.0500, 0.0100 ],
        [ 0.0100, 0.0100, 0.0500 ],
    ]
    f[PNOISE] = 0.1000
    f[ANOISE] = 0.0100