vpcPlot {nlmixr2plot}

VPC based on ui model

Description

VPC based on ui model

Usage

vpcPlot(
  fit,
  data = NULL,
  n = 300,
  bins = "jenks",
  n_bins = "auto",
  bin_mid = "mean",
  show = NULL,
  stratify = NULL,
  pred_corr = FALSE,
  pred_corr_lower_bnd = 0,
  pi = c(0.05, 0.95),
  ci = c(0.05, 0.95),
  uloq = fit$dataUloq,
  lloq = fit$dataLloq,
  log_y = FALSE,
  log_y_min = 0.001,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  smooth = TRUE,
  vpc_theme = NULL,
  facet = "wrap",
  scales = "fixed",
  labeller = NULL,
  vpcdb = FALSE,
  verbose = FALSE,
  ...,
  seed = 1009,
  idv = "time",
  cens = FALSE
)

vpcPlotTad(..., idv = "tad")

vpcCensTad(..., cens = TRUE, idv = "tad")

vpcCens(..., cens = TRUE, idv = "time")

Arguments

fit

nlmixr2 fit object

data

this is the data to use to augment the VPC fit. By default is the fitted data, (can be retrieved by getData), but it can be changed by specifying this argument.

n

Number of VPC simulations

bins

either "density", "time", or "data", "none", or one of the approaches available in classInterval() such as "jenks" (default) or "pretty", or a numeric vector specifying the bin separators.

n_bins

when using the "auto" binning method, what number of bins to aim for

bin_mid

either "mean" for the mean of all timepoints (default) or "middle" to use the average of the bin boundaries.

show

what to show in VPC (obs_dv, obs_ci, pi, pi_as_area, pi_ci, obs_median, sim_median, sim_median_ci)

stratify

character vector of stratification variables. Only 1 or 2 stratification variables can be supplied.

pred_corr

perform prediction-correction?

pred_corr_lower_bnd

lower bound for the prediction-correction

pi

simulated prediction interval to plot. Default is c(0.05, 0.95),

ci

confidence interval to plot. Default is (0.05, 0.95)

uloq

Number or NULL indicating upper limit of quantification. Default is NULL.

lloq

Number or NULL indicating lower limit of quantification. Default is NULL.

log_y

Boolean indicting whether y-axis should be shown as logarithmic. Default is FALSE.

log_y_min

minimal value when using log_y argument. Default is 1e-3.

xlab

label for x axis

ylab

label for y axis

title

title

smooth

"smooth" the VPC (connect bin midpoints) or show bins as rectangular boxes. Default is TRUE.

vpc_theme

theme to be used in VPC. Expects list of class vpc_theme created with function vpc_theme()

facet

either "wrap", "columns", or "rows"

scales

either "fixed" (default), "free_y", "free_x" or "free"

labeller

ggplot2 labeller function to be passed to underlying ggplot object

vpcdb

Boolean whether to return the underlying vpcdb rather than the plot

verbose

show debugging information (TRUE or FALSE)

...

Args sent to rxSolve

seed

an object specifying if and how the random number generator should be initialized

idv

Name of independent variable. For vpcPlot() and vpcCens() the default is "time" for vpcPlotTad() and vpcCensTad() this is "tad"

cens

is a boolean to show if this is a censoring plot or not. When cens=TRUE this is actually a censoring vpc plot (with vpcCens() and vpcCensTad()). When cens=FALSE this is traditional VPC plot (vpcPlot() and vpcPlotTad()).

Value

Simulated dataset (invisibly)

Author(s)

Matthew L. Fidler

Examples

one.cmt <- function() {
 ini({
   tka <- 0.45; label("Ka")
   tcl <- log(c(0, 2.7, 100)); label("Cl")
   tv <- 3.45; label("V")
   eta.ka ~ 0.6
   eta.cl ~ 0.3
   eta.v ~ 0.1
   add.sd <- 0.7; label("Additive residual error")
 })
 model({
   ka <- exp(tka + eta.ka)
   cl <- exp(tcl + eta.cl)
   v <- exp(tv + eta.v)
   linCmt() ~ add(add.sd)
 })
}

fit <-
  nlmixr2est::nlmixr(
    one.cmt,
    data = nlmixr2data::theo_sd,
    est = "saem",
    control = list(print = 0)
  )

vpcPlot(fit)