Cluster_Gauss_Newton_Bootstrap_method {CGNM} | R Documentation |

## Cluster_Gauss_Newton_Bootstrap_method

### Description

Conduct residual resampling bootstrap analyses using CGNM.

### Usage

```
Cluster_Gauss_Newton_Bootstrap_method(
CGNM_result,
nonlinearFunction,
num_bootstrapSample = 200,
indicesToUseAsInitialIterates = NA,
bootstrapType = 1,
...
)
```

### Arguments

`CGNM_result` |
(required input) |

`nonlinearFunction` |
(required input) |

`num_bootstrapSample` |
(default: 200) |

`indicesToUseAsInitialIterates` |
(default: NA) |

`bootstrapType` |
(default:1) |

`...` |
Further arguments to be supplied to nonlinearFunction |

### Value

list of a matrix X, Y,residual_history, initialX, bootstrapX, bootstrapY as well as a list runSetting.

X, Y, residual_history, initialX: identical to what was given as CGNM_result.

X:

*a num_bootstrapSample by n matrix*which stores the the X values that was sampled using residual resampling bootstrap analyses (In terms of model fitting this is the parameter combinations with variabilities that represent**parameter estimation uncertainties**.).Y:

*a num_bootstrapSample by m matrix*which stores the nonlinearFunction evaluated at the corresponding bootstrap analyses results in matrix bootstrapX above. In the context of model fitting each row corresponds to**the model simulations**.runSetting: identical to what is given as CGNM_result but in addition including num_bootstrapSample and indicesToUseAsInitialIterates.

### Examples

```
##lip-flop kinetics (an example known to have two distinct solutions)
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, num_iteration = 10, num_minimizersToFind = 100,
initial_lowerRange = c(0.1,0.1,0.1), initial_upperRange = c(10,10,10),
lowerBound=rep(0,3), ParameterNames=c("Ka","V1","CL_2"), saveLog = FALSE)
CGNM_bootstrap=Cluster_Gauss_Newton_Bootstrap_method(CGNM_result,
nonlinearFunction=model_analytic_function, num_bootstrapSample=100)
plot_paraDistribution_byHistogram(CGNM_bootstrap)
```

*CGNM*version 0.9.0 Index]