PopED (version 0.3.2)

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, groupsize_sim = 100,
  separate.groups = F, sample.times = T, sample.times.IPRED = F,
  sample.times.DV = F, PRED = T, IPRED = F, IPRED.lines = F,
  IPRED.lines.pctls = 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

poped.db

A PopED database.

model_num_points

How many extra observation rows should be created in the data frame for each group or individual per model. If used then the points are placed evenly between model_minxt and model_maxxt. This option is used by plot_model_prediction to simulate the response of the model on a finer grid then the defined design. If NULL then only the input design is used. Can be a single value or a vector the same length as the number of models.

groupsize_sim

How many individuals per group should be simulated when DV=TRUE or IPRED=TRUE to create prediction intervals?

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

Should we simulate individual predictions?

IPRED.lines

Should IPRED lines be drawn?

IPRED.lines.pctls

Should lines be drawn at the chosen percentiles of the IPRED values?

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?

DV

should we simulate observations?

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

...

arguments passed to evaluate.fim and ofv_fim.

Value

A ggplot object. If you would like to further edit this plot don't forget to load the ggplot2 library using library(ggplot2).

See Also

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

Other Graphics: plot_efficiency_of_windows

Other Simulation: model_prediction, plot_efficiency_of_windows

Examples

# NOT RUN {
## 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)

# }