table_profileLikelihoodConfidenceInterval {CGNM} | R Documentation |

## table_profileLikelihoodConfidenceInterval

### Description

Make table of confidence intervals that are approximated from the profile likelihood. First inspect profile likelihood plot and make sure the plot is smooth and has good enough resolution and the initial range is appropriate. Do not report this table without checking the profile likelihood plot.

### Usage

```
table_profileLikelihoodConfidenceInterval(
logLocation,
alpha = 0.25,
numBins = NA,
ParameterNames = NA,
ReparameterizationDef = NA,
pretty = FALSE,
Likelihood_function = Residual_function_def
)
```

### Arguments

`logLocation` |
(required input) |

`alpha` |
(default: 0.25) |

`numBins` |
(default: NA) |

`ParameterNames` |
(default: NA) |

`ReparameterizationDef` |
(default: NA) |

`pretty` |
(default: FALSE) |

`Likelihood_function` |
(default: Residual_function_def) |

### Value

*A ggplot object* including the violin plot, interquartile range and median, minimum and maximum.

### Examples

```
## Not run:
model_analytic_function=function(x){
observation_time=c(0.1,0.2,0.4,0.6,1,2,3,6,12)
Dose=1000
F=1
ka=x[1]
V1=x[2]
CL_2=x[3]
t=observation_time
Cp=ka*F*Dose/(V1*(ka-CL_2/V1))*(exp(-CL_2/V1*t)-exp(-ka*t))
log10(Cp)
}
observation=log10(c(4.91, 8.65, 12.4, 18.7, 24.3, 24.5, 18.4, 4.66, 0.238))
CGNM_result=Cluster_Gauss_Newton_method(
nonlinearFunction=model_analytic_function,
targetVector = observation,
initial_lowerRange = c(0.1,0.1,0.1), initial_upperRange = c(10,10,10),
num_iter = 10, num_minimizersToFind = 100, saveLog=TRUE)
table_profileLikelihoodConfidenceInterval("CGNM_log")
## End(Not run)
```

*CGNM*version 0.9.0 Index]