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 | 1.6306 | 0.6306 | 0.6306 |
| f[CL] | 1.0000 | 0.9667 | 0.0333 | 0.0333 |
| f[V1] | 20.0000 | 86.8189 | 66.8189 | 3.3409 |
Compare Noise f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[PNOISE] | 0.1000 | 0.2353 | 0.1353 | 1.3530 |
Compare Variance f[X]¶
| Variable Name | Starting Value | Fitted Value | Abs Change | Prop Change |
|---|---|---|---|---|
| f[KA_isv] | 0.0500 | 0.1541 | 0.1041 | 2.0829 |
| f[KA_isv;CL_isv] | 0.0100 | -0.0011 | 0.0111 | 1.1142 |
| f[KA_isv;V1_isv] | 0.0100 | -0.0022 | 0.0122 | 1.2157 |
| f[CL_isv;KA_isv] | 0.0100 | -0.0011 | 0.0111 | 1.1142 |
| f[CL_isv] | 0.0500 | 0.0000 | 0.0500 | 0.9997 |
| f[CL_isv;V1_isv] | 0.0100 | -0.0004 | 0.0104 | 1.0440 |
| f[V1_isv;KA_isv] | 0.0100 | -0.0022 | 0.0122 | 1.2157 |
| f[V1_isv;CL_isv] | 0.0100 | -0.0004 | 0.0104 | 1.0440 |
| f[V1_isv] | 0.0500 | 0.0428 | 0.0072 | 0.1432 |
Population simulated (sim) plots¶
(No population graphs were requested.)
Outputs¶
Fitted f[X] values (after fitting)¶
f[KA] = 1.6306
f[CL] = 0.9667
f[V1] = 86.8189
f[KA_isv,CL_isv,V1_isv] = [
[ 0.1541, -0.0011, -0.0022 ],
[ -0.0011, 0.0000, -0.0004 ],
[ -0.0022, -0.0004, 0.0428 ],
]
f[PNOISE] = 0.2353
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