acceptedMaxSSR {CGNM} | R Documentation |

## acceptedMaxSSR

### 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.

### Usage

```
acceptedMaxSSR(
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 positive real number* that is the maximum sum of squares residual (SSR) the algorithm has selected to accept.

### 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)
acceptedMaxSSR(CGNM_result)
```

*CGNM*version 0.9.0 Index]