table_profileLikelihoodConfidenceInterval {CGNM}

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) 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)

ParameterNames

(default: NA) A vector of strings the user can supply so that these names are used when making the plot. (Note if it set as NA or vector of incorrect length then the parameters are named as theta1, theta2, ... or as in ReparameterizationDef)

ReparameterizationDef

(default: NA) A vector of strings the user can supply definition of reparameterization where each string follows R syntax

pretty

(default: FALSE) TRUE or FALSE if true then the publication ready table will be an output

Likelihood_function

(default: Residual_function_def) a function that takes CGNM_result and initial then to calculate a quantity to be sketched in logscale e.g. SSR) this was implemented to conduct pos hoc drawing of the profile likelihood by providing the new definition of likelihood after all CGNM calculations are done.

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)