acceptedIndices {CGNM} | R Documentation |

## acceptedIndices

### Description

CGNM find multiple sets of minimizers of the nonlinear least squares (nls) problem by solving nls from various initial iterates. Although CGNM is shown to be robust compared to other conventional multi-start algorithms, not all initial iterates minimizes successfully. By assuming sum of squares residual (SSR) follows the chai-square distribution we first reject the approximated minimiser who SSR is statistically significantly worse than the minimum SSR found by the CGNM. Then use elbow-method (a heuristic often used in mathematical optimisation to balance the quality and the quantity of the solution found) to find the "acceptable" maximum SSR. This function outputs the indices of acceptable approximate minimizers of the nonlinear least squares problem found by the CGNM.

### Usage

```
acceptedIndices(
CGNM_result,
cutoff_pvalue = 0.05,
numParametersIncluded = NA,
useAcceptedApproximateMinimizers = TRUE,
algorithm = 2
)
```

### Arguments

`CGNM_result` |
(required input) |

`cutoff_pvalue` |
(default: 0.05) |

`numParametersIncluded` |
(default: NA) |

`useAcceptedApproximateMinimizers` |
(default: TRUE) |

`algorithm` |
(default: 2) |

### Value

*A vector of natural number* that contains the indices of accepted approximate minimizers found by CGNM.

### Examples

```
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 = FALSE)
acceptedIndices(CGNM_result)
```

*CGNM*version 0.9.0 Index]