# Depot One Comp PK with BLQ observations set to LLQ

[Generated automatically as a Fitting summary]

## Inputs

### Description

Name: | blq_pk_norm_fit
Title: | Depot One Comp PK with BLQ observations set to LLQ
Author: | J.R. Hartley
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`
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

    122013.4799

which required 1.30 iterations and took 333.52 seconds

### Final fitted fixed effects

    f[KA] = 0.8984
    f[CL] = 0.9553
    f[V1] = 88.4773
    f[KA_isv,CL_isv,V1_isv] = [
        [ 0.0000, -0.0001, 0.0005 ],
        [ -0.0001, 0.0007, -0.0063 ],
        [ 0.0005, -0.0063, 0.0600 ],
    ]
    f[PNOISE] = 0.2339
    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 | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA] | 1.0000 | 0.8984 | 0.1016 | 0.1016
f[CL] | 1.0000 | 0.9553 | 0.0447 | 0.0447
f[V1] | 20.0000 | 88.4773 | 3.4239 | 68.4773

### Compare Noise f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[PNOISE] | 0.1000 | 0.2339 | 1.3392 | 0.1339

### Compare Variance f[X]

Variable Name | Starting Value | Fitted Value | Prop Change | Abs Change
--- | --- | --- | --- | ---
f[KA_isv] | 0.0500 | 0.0000 | 0.9999 | 0.0500
f[KA_isv;CL_isv] | 0.0100 | -0.0001 | 1.0057 | 0.0101
f[KA_isv;V1_isv] | 0.0100 | 0.0005 | 0.9457 | 0.0095
f[CL_isv;KA_isv] | 0.0100 | -0.0001 | 1.0057 | 0.0101
f[CL_isv] | 0.0500 | 0.0007 | 0.9868 | 0.0493
f[CL_isv;V1_isv] | 0.0100 | -0.0063 | 1.6285 | 0.0163
f[V1_isv;KA_isv] | 0.0100 | 0.0005 | 0.9457 | 0.0095
f[V1_isv;CL_isv] | 0.0100 | -0.0063 | 1.6285 | 0.0163
f[V1_isv] | 0.0500 | 0.0600 | 0.1997 | 0.0100