plot_goodnessOfFit {CGNM} | R Documentation |
Make goodness of fit plots to assess the model-fit and bias in residual distribution.
Explanation of the terminologies in terms of PBPK model fitting to the time-course drug concentration measurements:
"independent variable" is time
"dependent variable" is the concentration.
"Residual" is the difference between the measured concentration and the model simulation with the parameter fond by the CGNM.
"m" is number of observations
plot_goodnessOfFit(
CGNM_result,
plotType = 1,
plotRank = c(1),
independentVariableVector = NA,
dependentVariableTypeVector = NA,
absResidual = FALSE
)
CGNM_result |
(required input) A list stores the computational result from Cluster_Gauss_Newton_method() function in CGNM package. |
plotType |
(default: 1) 1,2 or 3 |
plotRank |
(default: c(1)) a vector of integers |
independentVariableVector |
(default: NA) a vector of numerics of length m |
dependentVariableTypeVector |
(default: NA) a vector of text of length m |
absResidual |
(default: FALSE) TRUE or FALSE If TRUE plot absolute values of the residual. |
A ggplot object of the goodness of fit plot.
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=10^x[1]
V1=10^x[2]
CL_2=10^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 = rep(0.01,3), initial_upperRange = rep(100,3),
lowerBound=rep(0,3), ParameterNames = c("Ka","V1","CL"),
num_iter = 10, num_minimizersToFind = 100, saveLog = FALSE)
plot_goodnessOfFit(CGNM_result)
plot_goodnessOfFit(CGNM_result,
independentVariableVector=c(0.1,0.2,0.4,0.6,1,2,3,6,12))