# Depot + One compartment PK with BLQ

[Generated automatically as a Fitting summary]

## Model Description

Name:

  blq_pk

Title:

  Depot + One compartment PK with BLQ

Author:

  PoPy for PK/PD

Abstract:

Depot One Comp PK model, with BLQ (below level of quantification) observations.

Keywords:

  tutorial; pk; advan4; dep_two_cmp; blq

Input Script:

  `blq_pk_fit.pyml`

Diagram:

## Comparison

### Compare Main f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA] | 1.0000 | 0.2060 | 0.7940 | 0.7940
f[CL] | 1.0000 | 1.9987 | 0.9987 | 0.9987
f[V1] | 20.0000 | 50.9527 | 30.9527 | 1.5476

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1000 | 0.1469 | 0.0469 | 0.4694

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change
--- | --- | --- | --- | ---
f[KA_isv] | 0.0500 | 0.0532 | 0.0032 | 0.0632
f[KA_isv;CL_isv] | 0.0100 | 0.0111 | 0.0011 | 0.1131
f[KA_isv;V1_isv] | 0.0100 | -0.0286 | 0.0386 | 3.8623
f[CL_isv;KA_isv] | 0.0100 | 0.0111 | 0.0011 | 0.1131
f[CL_isv] | 0.0500 | 0.0289 | 0.0211 | 0.4222
f[CL_isv;V1_isv] | 0.0100 | 0.0239 | 0.0139 | 1.3938
f[V1_isv;KA_isv] | 0.0100 | -0.0286 | 0.0386 | 3.8623
f[V1_isv;CL_isv] | 0.0100 | 0.0239 | 0.0139 | 1.3938
f[V1_isv] | 0.0500 | 0.0642 | 0.0142 | 0.2830

### Individual simulated (sim) plots

Alternatively see All simulated_sim graph plots

### Population simulated (sim) plots

(No population graphs were requested.)

## Outputs

### Final objective value

    -786.5417

which required 1.14 iterations and took 166.96 seconds

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

    f[KA] = 0.2060
    f[CL] = 1.9987
    f[V1] = 50.9527
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0532, 0.0111, -0.0286 ],
        [ 0.0111, 0.0289, 0.0239 ],
        [ -0.0286, 0.0239, 0.0642 ],
    ]
    f[PNOISE] = 0.1469
    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:

  `cx_obs_params.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