vpc (version 1.2.4)

vpc_cat: VPC function for categorical

Description

Creates a VPC plot from observed and simulation data for categorical variables.

Usage

vpc_cat(
  sim = NULL,
  obs = NULL,
  psn_folder = NULL,
  bins = "jenks",
  n_bins = "auto",
  bin_mid = "mean",
  obs_cols = NULL,
  sim_cols = NULL,
  software = "auto",
  show = NULL,
  ci = c(0.05, 0.95),
  uloq = NULL,
  lloq = NULL,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  smooth = TRUE,
  vpc_theme = NULL,
  facet = "wrap",
  labeller = NULL,
  vpcdb = FALSE,
  verbose = FALSE
)

Value

a list containing calculated VPC information (when vpcdb=TRUE), or a ggplot2 object (default)

Arguments

sim

this is usually a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm. However it can also be an object like a nlmixr or xpose object

obs

a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm

psn_folder

instead of specifying "sim" and "obs", specify a PsN-generated VPC-folder

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.

obs_cols

list for mapping observation data columns, e.g. `list(dv = "DV", id = "ID", idv = "TIME", pred="PRED")`

sim_cols

list for mapping simulation data columns, e.g. `list(dv = "DV", id = "ID", idv = "TIME", pred="PRED")`

software

name of software platform using (e.g. nonmem, phoenix)

show

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

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.

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"

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)

See Also

vpc, vpc_tte, vpc_cens

Examples

## See vpc.ronkeizer.com for more documentation and examples
library(vpc)

# simple function to simulate categorical data for single individual
sim_id <- function(id = 1) {
  n <- 10
  logit <- function(x) exp(x) / (1+exp(x))
  data.frame(id = id, time = seq(1, n, length.out = n),
             dv = round(logit((1:n) - n/2 + rnorm(n, 0, 1.5))) )
}
## simple function to simulate categorical data for a trial
sim_trial <- function(i = 1, n = 20) { # function to simulate categorical data for a trial
  data.frame(sim = i, do.call("rbind", lapply(1:n, sim_id)))
}

## simulate single trial for 20 individuals
obs <- sim_trial(n = 20)

## simulate 200 trials of 20 individuals
sim <- do.call("rbind", lapply(1:200, sim_trial, n = 20))

## Plot categorical VPC
vpc_cat(sim = sim, obs = obs)