Plot model predictions

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

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,
  model.names = NULL,
  DV.mean.sd = FALSE,
  PI = FALSE,
  PI_alpha = 0.3,
  ...
)

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 transparency 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 transparency 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
model.names	A vector of names of the response model/s (the length of the vector should be equal to the number of response models). It is Null by default.
DV.mean.sd	Plot the mean and standard deviation of simulated observations.
PI	Plot prediction intervals for the expected data given the model. Predictions are based on first-order approximations to the model variance and a normality assumption of that variance. As such these computations are more approximate than using `DV=T` and `groupsize_sim = some large number`.
PI_alpha	The transparency of the PI.
...	Additional arguments passed to the `model_prediction` function.

Value

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

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
#> function (model_switch, xt, parameters, poped.db) 
#> {
#>     with(as.list(parameters), {
#>         y = xt
#>         N = floor(xt/TAU) + 1
#>         y = (DOSE * Favail/V) * (KA/(KA - CL/V)) * (exp(-CL/V * 
#>             (xt - (N - 1) * TAU)) * (1 - exp(-N * CL/V * TAU))/(1 - 
#>             exp(-CL/V * TAU)) - exp(-KA * (xt - (N - 1) * TAU)) * 
#>             (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU)))
#>         return(list(y = y, poped.db = poped.db))
#>     })
#> }
#> <bytecode: 0x7fe25cadfcb0>
#> <environment: namespace:PopED>

## -- 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)
#> Warning: cannot open file 'sfg': No such file or directory
#> Error in file(filename, "r", encoding = encoding): cannot open the connection

##  create plot of model without variability 
plot_model_prediction(poped.db)
#> Error in model_prediction(poped.db, model_num_points = model_num_points,     PI = PI, ...): object 'poped.db' not found

##  create plot of model with variability by simulating from OMEGA and SIGMA
plot_model_prediction(poped.db,IPRED=TRUE,DV=TRUE)
#> Error in model_prediction(poped.db, model_num_points = model_num_points,     PI = PI, ...): object 'poped.db' not found

##  create plot of model with variability by 
##  computing the expected variance (using an FO approximation) 
##  and then computing a prediction interval 
##  based on an assumption of normality
##  computation is faster but less accurate 
##  compared to using DV=TRUE (and groupsize_sim = 500)
plot_model_prediction(poped.db,PI=TRUE)
#> Error in model_prediction(poped.db, model_num_points = model_num_points,     PI = PI, ...): object 'poped.db' not found

##-- Model: One comp first order absorption + inhibitory imax
## -- works for both mutiple and single dosing  
ff <- function(model_switch,xt,parameters,poped.db){
  with(as.list(parameters),{
    
    y=xt
    MS <- model_switch
    
    # PK model
    N = floor(xt/TAU)+1
    CONC=(DOSE*Favail/V)*(KA/(KA - CL/V)) * 
      (exp(-CL/V * (xt - (N - 1) * TAU)) * (1 - exp(-N * CL/V * TAU))/(1 - exp(-CL/V * TAU)) - 
         exp(-KA * (xt - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU)))  
    
    # PD model
    EFF = E0*(1 - CONC*IMAX/(IC50 + CONC))
    
    y[MS==1] = CONC[MS==1]
    y[MS==2] = EFF[MS==2]
    
    return(list( y= y,poped.db=poped.db))
  })
}

## -- parameter definition function 
sfg <- function(x,a,bpop,b,bocc){
  parameters=c( V=bpop[1]*exp(b[1]),
                KA=bpop[2]*exp(b[2]),
                CL=bpop[3]*exp(b[3]),
                Favail=bpop[4],
                DOSE=a[1],
                TAU = a[2],
                E0=bpop[5]*exp(b[4]),
                IMAX=bpop[6],
                IC50=bpop[7])
  return( parameters ) 
}


## -- Residual Error function
feps <- function(model_switch,xt,parameters,epsi,poped.db){
  returnArgs <- ff(model_switch,xt,parameters,poped.db) 
  y <- returnArgs[[1]]
  poped.db <- returnArgs[[2]]
  
  MS <- model_switch
  
  pk.dv <- y*(1+epsi[,1])+epsi[,2]
  pd.dv <-  y*(1+epsi[,3])+epsi[,4]
  
  y[MS==1] = pk.dv[MS==1]
  y[MS==2] = pd.dv[MS==2]
  
  return(list( y= y,poped.db =poped.db )) 
}

poped.db <- 
  create.poped.database(
    ff_fun="ff",
    fError_fun="feps",
    fg_fun="sfg",
    groupsize=20,
    m=3,
    bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9,
           E0=1120,IMAX=0.807,IC50=0.0993),  
    notfixed_bpop=c(1,1,1,0,1,1,1),
    d=c(V=0.09,KA=0.09,CL=0.25^2,E0=0.09), 
    sigma=c(0.04,5e-6,0.09,100),
    notfixed_sigma=c(0,0,0,0),
    xt=c( 1,2,8,240,240,1,2,8,240,240),
    minxt=c(0,0,0,240,240,0,0,0,240,240),
    maxxt=c(10,10,10,248,248,10,10,10,248,248),
    discrete_xt = list(0:248),
    G_xt=c(1,2,3,4,5,1,2,3,4,5),
    bUseGrouped_xt=1,
    model_switch=c(1,1,1,1,1,2,2,2,2,2),
    a=list(c(DOSE=20,TAU=24),c(DOSE=40, TAU=24),c(DOSE=0, TAU=24)),
    maxa=c(DOSE=200,TAU=40),
    mina=c(DOSE=0,TAU=2),
    ourzero=0)
#> Error in do.call(poped.db$model$fg_pointer, list(0, 0, 0, 0, zeros(poped.db$parameters$NumDocc,     poped.db$parameters$NumOcc))): 'what' must be a function or character string

##  create plot of model and design 
plot_model_prediction(poped.db,facet_scales="free",
                      model.names = c("PK","PD"))
#> Error in model_prediction(poped.db, model_num_points = model_num_points,     PI = PI, ...): object 'poped.db' not found

##  create plot of model with variability by  
##  computing the expected variance (using an FO approximation) 
##  and then computing a prediction interval 
##  based on an assumption of normality
##  computation is faster but less accurate 
##  compared to using DV=TRUE (and groupsize_sim = 500)
plot_model_prediction(poped.db,facet_scales="free",
                      model.names = c("PK","PD"),
                      PI=TRUE,
                      separate.groups = TRUE)
#> Error in model_prediction(poped.db, model_num_points = model_num_points,     PI = PI, ...): object 'poped.db' not found

Arguments

Value

See also

Examples