CGNM (version 0.9.0)

suggestInitialUpperRange: suggestInitialUpperRange

Description

Suggest initial upper range based on the profile likelihood. The user can re-run CGNM with this suggested initial range so that to improve the convergence.

Usage

suggestInitialUpperRange(logLocation, alpha = 0.25, numBins = NA)

Value

A numerical vector of suggested initial upper range based on profile likelihood.

Arguments

logLocation

(required input) A string or a list of strings of folder directory where CGNM computation log files exist.

alpha

(default: 0.25) a number between 0 and 1 level of significance used to derive the confidence interval.

numBins

(default: NA) A positive integer SSR surface is plotted by finding the minimum SSR given one of the parameters is fixed and then repeat this for various values. numBins specifies the number of different parameter values to fix for each parameter. (if set NA the number of bins are set as num_minimizersToFind/10)

Examples

if (FALSE) {
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)

suggestInitialLowerRange("CGNM_log")
}