Depot One Comp PK with BLQ observations set to 0.5*LLQ¶
[Generated automatically as a Fitting summary]
Model Description¶
| Name: | blq_pk_norm_fit_half |
|---|---|
| Title: | Depot One Comp PK with BLQ observations set to 0.5*LLQ |
| Author: | PoPy for PK/PD |
| Abstract: |
Depot One Comp PK model, with BLQ (below level of quantification)
observations set to 0.5*LLQ (lower limit of quantification).
| Keywords: | tutorial; pk; advan4; dep_two_cmp; blq |
|---|---|
| Input Script: | blq_pk_norm_fit_half.pyml |
| Diagram: |
Comparison¶
Compare Main f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA] | 1.0000 | 1.2625 | 0.2625 | 0.2625 |
| f[CL] | 1.0000 | 1.6570 | 0.6570 | 0.6570 |
| f[V1] | 20.0000 | 80.1152 | 60.1152 | 3.0058 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[PNOISE] | 0.1000 | 0.3301 | 0.2301 | 2.3010 |
Compare Variance f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA_isv] | 0.0500 | 0.7148 | 0.6648 | 13.2962 |
| f[KA_isv;CL_isv] | 0.0100 | 0.0848 | 0.0748 | 7.4800 |
| f[KA_isv;V1_isv] | 0.0100 | 0.2052 | 0.1952 | 19.5219 |
| f[CL_isv;KA_isv] | 0.0100 | 0.0848 | 0.0748 | 7.4800 |
| f[CL_isv] | 0.0500 | 0.0101 | 0.0399 | 0.7983 |
| f[CL_isv;V1_isv] | 0.0100 | 0.0244 | 0.0144 | 1.4390 |
| f[V1_isv;KA_isv] | 0.0100 | 0.2052 | 0.1952 | 19.5219 |
| f[V1_isv;CL_isv] | 0.0100 | 0.0244 | 0.0144 | 1.4390 |
| f[V1_isv] | 0.0500 | 0.0591 | 0.0091 | 0.1824 |
Population simulated (sim) plots¶
(No population graphs were requested.)
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 1.2625
f[CL] = 1.6570
f[V1] = 80.1152
f[KA_isv,CL_isv,V1_isv] = [
[ 0.7148, 0.0848, 0.2052 ],
[ 0.0848, 0.0101, 0.0244 ],
[ 0.2052, 0.0244, 0.0591 ],
]
f[PNOISE] = 0.3301
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