PopED (version 0.1.1)

plot_model_prediction: Plot model predictions

Description

Function plots model predictions for the typical value in the population, individual predictions and data predictions.

Usage

plot_model_prediction(poped.db, model_num_points = 100, separate.groups = F,
  sample.times = T, sample.times.IPRED = F, sample.times.DV = F,
  PRED = T, IPRED = F, IPRED.lines = F, alpha.IPRED.lines = 0.1,
  alpha.IPRED = 0.3, sample.times.size = 4, DV = F, alpha.DV = 0.3,
  DV.lines = F, DV.points = F, alpha.DV.lines = 0.3,
  alpha.DV.points = 0.3, sample.times.DV.points = F,
  sample.times.DV.lines = F, alpha.sample.times.DV.points = 0.3,
  alpha.sample.times.DV.lines = 0.3, y_lab = "Model Predictions",
  facet_scales = "fixed", facet_label_names = T, ...)

Arguments

separate.groups

Should there be separate plots for each group.

sample.times

Should sample times be shown on the plots.

sample.times.IPRED

Should sample times be shown based on the IPRED y-values.

sample.times.DV

Should sample times be shown based on the DV y-values.

PRED

Should a PRED line be drawn.

IPRED.lines

Should IPRED lines be drawn?

alpha.IPRED.lines

What should the transparency for the IPRED.lines be?

alpha.IPRED

What should the tranparency of the IPRED CI?

sample.times.size

What should the size of the sample.times be?

alpha.DV

What should the tranparency of the DV CI?

DV.lines

Should DV lines be drawn?

DV.points

Should DV points be drawn?

alpha.DV.lines

What should the transparency for the DV.lines be?

alpha.DV.points

What should the transparency for the DV.points be?

sample.times.DV.points

TRUE or FALSE.

sample.times.DV.lines

TRUE or FALSE.

alpha.sample.times.DV.points

What should the transparency for the sample.times.DV.points be?

alpha.sample.times.DV.lines

What should the transparency for the sample.times.DV.lines be?

y_lab

The label of the y-axis.

facet_scales

Can be "free", "fixed", "free_x" or "free_y"

facet_label_names

TRUE or FALSE

poped.db

A PopED database.

...

arguments passed to evaluate.fim and ofv_fim.

model_num_points

How many points should be plotted. If not a number then the design in poped.db is used.

IPRED

Should we simulate individual predictions?

DV

should we simulate observations?

Value

• A ggplot2 object.

See Also

Other Graphics: plot_efficiency_of_windows

Other Simulation: model_prediction; plot_efficiency_of_windows

Other evaluate_design: evaluate.fim; get_rse; model_prediction; plot_efficiency_of_windows

Examples

## 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. 

library(PopED)

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

##  create plot of model without variability 
plot_model_prediction(poped.db)

##  create plot of model with variability 
plot_model_prediction(poped.db,IPRED=TRUE,DV=TRUE)