# Depot + One compartment PK with BLQ

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | blq_pk
Title: | Depot + One compartment PK with BLQ
Author: | J.R. Hartley
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`
Input Data: | `synthetic_data.csv`
Diagram: |

### Initial fixed effect estimates

    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

## Outputs

### Final objective value

    -732.3632

which required N. iterations and took 268.74 seconds

### Final fitted fixed effects

    f[KA] = 0.2185
    f[CL] = 1.9944
    f[V1] = 50.4071
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0203, 0.0106, 0.0251 ],
        [ 0.0106, 0.0126, 0.0145 ],
        [ 0.0251, 0.0145, 0.1816 ],
    ]
    f[PNOISE] = 0.1509
    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)`

## Plots

### Dense sim plots

Alternatively see All dense_sim graph plots

## Comparison

### Compare Main f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA] | 0.2185 | 1.0000 | 0.7815 | 0.7815
f[CL] | 1.9944 | 1.0000 | 0.9944 | 0.9944
f[V1] | 50.4071 | 20.0000 | 1.5204 | 30.4071

### Compare Noise f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1509 | 0.1000 | 0.5089 | 0.0509

### Compare Variance f[X]

Variable Name | Fitted Value | Starting Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA_isv] | 0.0203 | 0.0500 | 0.5940 | 0.0297
f[KA_isv;CL_isv] | 0.0106 | 0.0100 | 0.0639 | 0.0006
f[KA_isv;V1_isv] | 0.0251 | 0.0100 | 1.5135 | 0.0151
f[CL_isv;KA_isv] | 0.0106 | 0.0100 | 0.0639 | 0.0006
f[CL_isv] | 0.0126 | 0.0500 | 0.7486 | 0.0374
f[CL_isv;V1_isv] | 0.0145 | 0.0100 | 0.4461 | 0.0045
f[V1_isv;KA_isv] | 0.0251 | 0.0100 | 1.5135 | 0.0151
f[V1_isv;CL_isv] | 0.0145 | 0.0100 | 0.4461 | 0.0045
f[V1_isv] | 0.1816 | 0.0500 | 2.6318 | 0.1316