PopED (version 0.3.2)

plot_efficiency_of_windows: Plot the efficiency of windows

Description

Function plots the efficiency of windows around the optimal design points. The function samples from a uniform distribution around the optimal design points for each group (or each individual with deviate_by_id=TRUE, with slower calculation times) and compares the results with the optimal design. The maximal and minimal allowed values for all design variables as defined in poped.db are respected (e.g. poped.db$design_space$minxt and poped.db$design_space$maxxt).

Usage

plot_efficiency_of_windows(poped.db, xt_windows = NULL,
  xt_plus = xt_windows, xt_minus = xt_windows, iNumSimulations = 100,
  y_eff = TRUE, y_rse = TRUE,
  ofv_calc_type = poped.db$settings$ofv_calc_type, mean_line = TRUE,
  mean_color = "red", deviate_by_id = FALSE, ...)

Arguments

poped.db

A poped database

xt_windows

The distance on one direction from the optimal sample times. Can be a number or a matrix of the same size as the xt matrix found in poped.db$design$xt.

xt_plus

The upper distance from the optimal sample times (xt + xt_plus). Can be a number or a matrix of the same size as the xt matrix found in poped.db$design$xt.

xt_minus

The lower distance from the optimal sample times (xt - xt_minus). Can be a number or a matrix of the same size as the xt matrix found in poped.db$design$xt.

iNumSimulations

The number of design simulations to make within the specified windows.

y_eff

Should one of the plots created have efficiency on the y-axis?

y_rse

Should created plots include the relative standard error of each parameter as a value on the y-axis?

ofv_calc_type

OFV calculation type for FIM

• 1 = "D-optimality". Determinant of the FIM: det(FIM)

• 2 = "A-optimality". Inverse of the sum of the expected parameter variances: 1/trace_matrix(inv(FIM))

• 4 = "lnD-optimality". Natural logarithm of the determinant of the FIM: log(det(FIM))

• 6 = "Ds-optimality". Ratio of the Determinant of the FIM and the Determinant of the uninteresting rows and columns of the FIM: det(FIM)/det(FIM_u)

• 7 = Inverse of the sum of the expected parameter RSE: 1/sum(get_rse(FIM,poped.db,use_percent=FALSE))

mean_line

Should a mean value line be created?

mean_color

The color of the mean value line.

deviate_by_id

Should the computations look at deviations per individual instead of per group?

...

Extra arguments passed to evaluate.fim

Value

A ggplot object.

See Also

Other evaluate_design: evaluate.fim, evaluate_design, get_rse, model_prediction, plot_model_prediction

Other Graphics: plot_model_prediction

Other Simulation: model_prediction, plot_model_prediction

Examples

# NOT RUN {
library(PopED)

############# START #################
## Create PopED database
## (warfarin example)
#####################################

## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation 
##   for population pharmacokinetics-pharmacodynamics studies", 
##   Br. J. Clin. Pharm., 2014. 

## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL

## -- parameter definition function 
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
  parameters=c(CL=bpop[1]*exp(b[1]),
               V=bpop[2]*exp(b[2]),
               KA=bpop[3]*exp(b[3]),
               Favail=bpop[4],
               DOSE=a[1])
  return(parameters) 
}

## -- Define initial design  and design space
poped.db <- create.poped.database(ff_file="ff.PK.1.comp.oral.sd.CL",
                                  fg_file="sfg",
                                  fError_file="feps.prop",
                                  bpop=c(CL=0.15, V=8, KA=1.0, Favail=1), 
                                  notfixed_bpop=c(1,1,1,0),
                                  d=c(CL=0.07, V=0.02, KA=0.6), 
                                  sigma=0.01,
                                  groupsize=32,
                                  xt=c( 0.5,1,2,6,24,36,72,120),
                                  minxt=0,
                                  maxxt=120,
                                  a=70)

############# END ###################
## Create PopED database
## (warfarin example)
#####################################


# Examine efficiency of sampling windows
plot_efficiency_of_windows(poped.db,xt_windows=0.5)

plot_efficiency_of_windows(poped.db,
                           xt_plus=c( 0.5,1,2,1,2,3,7,1),
                           xt_minus=c( 0.1,2,5,4,2,3,6,2))

# }
# NOT RUN {
  
  plot_efficiency_of_windows(poped.db,xt_windows=c( 0.5,1,2,1,2,3,7,1))
  
  
  plot_efficiency_of_windows(poped.db,
                             xt_plus=c( 0.5,1,2,1,2,3,7,1),
                             xt_minus=c( 0.1,2,5,4,2,3,6,2),
                             y_rse=FALSE)
  
  plot_efficiency_of_windows(poped.db,
                             xt_plus=c( 0.5,1,2,1,2,3,7,1),
                             xt_minus=c( 0.1,2,5,4,2,3,6,2),
                             y_eff=FALSE)
# }
# NOT RUN {
# }