plot_profileLikelihood {CGNM}

plot_profileLikelihood

Description

Draw profile likelihood surface using the function evaluations conducted during CGNM computation. Note plot_SSRsurface can only be used when log is saved by setting saveLog=TRUE option when running Cluster_Gauss_Newton_method(). The grey horizontal line is the threshold for 95

Usage

plot_profileLikelihood(
  logLocation,
  alpha = 0.25,
  numBins = NA,
  ParameterNames = NA,
  ReparameterizationDef = NA,
  showInitialRange = TRUE,
  Likelihood_function = Residual_function_def
)

Arguments

logLocation

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

alpha

(default: 0.25) a number between 0 and 1 level of significance (used to draw horizontal line on the profile likelihood).

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

showInitialRange

(default: TRUE) TRUE or FALSE if TRUE then the initial range appears in the plot.

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)

plot_profileLikelihood("CGNM_log")

## End(Not run)