mapbayr (version 0.10.2)

augment.mapbayests: Compute full PK profile prediction from mapbayr estimates.

Description

Compute full PK profile prediction from mapbayr estimates.

Usage

# S3 method for mapbayests
augment(
  x,
  data = NULL,
  start = NULL,
  end = NULL,
  delta = NULL,
  ci = FALSE,
  ci_width = 90,
  ci_method = "delta",
  ci_sims = 500,
  ...
)

Value

a mapbayests object, augmented of an aug_tab data.frame.

Arguments

x

A mapbayests object.

data

dataset to pass to mrgsolve for simulation (default is dataset used for estimation)

start, end, delta

start, end and delta of simulation time passed to mrgsim() (see details)

ci

a logical. If TRUE, compute a confidence interval around the prediction (default is FALSE)

ci_width

a number between 0 and 100, width of the confidence interval (default is "90" for a 90%CI)

ci_method

method to compute the confidence interval. Can be "delta" (the default) to use the Delta approximation. Alternatively "simulations" for a more accurate approach, but also more time-consuming.

ci_sims

number of replicates to simulate in order to derive the confidence interval (default is 500)

...

additional arguments passed to mrgsim()

Details

This function is called in the background by plot() in order to simulate the full PK profile, and return a mapbayests object with an additional aug_tab data.frame inside. The latter is used with by the plot method. The time grid, for each PK profile (i.e. patient) is defaulted with the minimum time in the dataset for start and the maximum time in the dataset +20% for end. delta is a power of 10 (e.g. 0.1, 1, 10 etc...), automatically chosen to render visually appealing graphs with a reasonable computing time (about 200 time points). Additional arguments can be passed to mrgsim() through .... Note that recsort is set to 3 (see mrgsolve documentation for more details).

Examples

#x is the result of `mapbayest()`.
#Default plot is returned by:
# plot(x)
#Argument passed to `plot()` are passed to `augment()` in the background:
# plot(x, end = 240, ci = TRUE)
#Save the augmented object if simulation time is long
# x2 <- augment(x, ci = TRUE, ci_method = "simulations", ci_sims = 10000) %>%
# plot(x2)