Title:	Create Visual Predictive Checks
Version:	1.2.4
Date:	2026-02-10
Maintainer:	Ron Keizer <ron@insight-rx.com>
Description:	Visual predictive checks are a commonly used diagnostic plot in pharmacometrics, showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. The package can generate VPCs for continuous, categorical, censored, and (repeated) time-to-event data.
Depends:	R (≥ 3.1.0)
Imports:	classInt, dplyr, ggplot2, MASS, readr, rlang, stringr, survival, tidyr
License:	MIT + file LICENSE
LazyData:	true
URL:	https://github.com/ronkeizer/vpc
Suggests:	covr, knitr, testthat (≥ 3.0.0)
RoxygenNote:	7.3.3
Encoding:	UTF-8
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-02-13 02:45:41 UTC; ronkeizer
Author:	Ron Keizer [aut, cre], Bill Denney [aut]
Repository:	CRAN
Date/Publication:	2026-02-17 07:20:02 UTC

vpc: Create Visual Predictive Checks

Description

Visual predictive checks are a commonly used diagnostic plot in pharmacometrics, showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. The package can generate VPCs for continuous, categorical, censored, and (repeated) time-to-event data.

Author(s)

Maintainer: Ron Keizer ron@insight-rx.com

Authors:

• Bill Denney wdenney@humanpredictions.com (ORCID)

See Also

Useful links:

• https://github.com/ronkeizer/vpc

Add noise / residual error to data

Description

Add noise / residual error to data

Usage

add_noise(x, ruv = list(proportional = 0, additive = 0, exponential = 0))

Arguments

Examples

library(dplyr)
ipred <- c(10, 8, 6, 4, 2, 0) %>% add_noise(ruv = list(proportional = 0.1, additive = 0.2))

Add sim index number

Description

Add simulation index number to simulation when not present

Usage

add_sim_index_number(sim, id = "id", sim_label = "sim")

Arguments

Adds stratification to data set

Description

Adds stratification to data set

Usage

add_stratification(dat, stratify, verbose = FALSE)

Arguments

Create a vpcdb object, and standardize parameter checking

Description

Create a vpcdb object, and standardize parameter checking

Usage

as_vpcdb(..., type = NULL, facet = NULL, scales = NULL, labeller = NULL)

Arguments

Value

A vpcdb object which is simply a named list with some of the values checked for correctness

Calculate appropriate bin separators for vpc

Description

This function calculates bin separators either using R's native binning approaches available in the classInt library such as 'kmeans', 'jenks', 'pretty' etc. Alternatively, a custom approach is available which is based on finding the nadirs in the density functions for the independent variable. Default approach is k-means clustering.

Usage

auto_bin(dat, type = "kmeans", n_bins = 8, verbose = FALSE, ...)

## S3 method for class 'numeric'
auto_bin(dat, type = "kmeans", n_bins = 8, verbose = FALSE, ...)

## S3 method for class 'data.frame'
auto_bin(dat, type = "kmeans", n_bins = 8, verbose = FALSE, ...)

Arguments

Value

A vector of bin separators

Function to bin data based on a vector of bin separators, e.g. for use in VPC

Description

Function to bin data based on a vector of bin separators, e.g. for use in VPC

Usage

bin_data(x, bins = c(0, 3, 5, 7), idv = "time", labeled = F)

Arguments

Perform prediction-correction

Description

Perform prediction-correction

Usage

calc_pred_corr_continuous(
  sim,
  obs,
  pred_corr,
  pred_corr_lower_bnd,
  cols,
  verbose
)

Arguments

Value

A list with "sim" and "obs" (with pred_corr performed, if requested)

Calculate aggregate statistics for simulated and observed VPC data

Description

Calculate aggregate statistics for simulated and observed VPC data

Usage

calc_vpc_continuous(sim, obs, loq, pi, ci, stratify, bins, bin_mid, verbose)

Arguments

Value

A list with "vpc_dat" and "aggr_obs"

Check whether stratification columns are available

Description

Check whether stratification columns are available

Usage

check_stratification_columns_available(data, stratify, type = "observation")

Arguments

Value

TRUE or raise an error about the missing columns

Compute Kaplan-Meier statistics

Description

Compute Kaplan-Meier statistics

Usage

compute_kaplan(
  dat,
  strat = "strat",
  reverse_prob = FALSE,
  rtte_conditional = TRUE,
  ci = NULL
)

Arguments

Compute KMMC statistics

Description

Kaplan-Meier Mean Covariate plots are a simulation-based diagnostic to study the influence of covariates and identify potential model misspecification.

Usage

compute_kmmc(dat, strat = NULL, reverse_prob = FALSE, kmmc = "DOSE")

Arguments

Create new vpc theme

Description

Create new vpc theme

Usage

create_vpc_theme(...)

Arguments

Value

The vpc theme

Define bins for many types of data

Description

Define bins for many types of data

Usage

define_bins(obs, sim, bins, n_bins, verbose = FALSE)

define_bins_tte(obs, sim, bins, n_bins, kmmc, verbose = FALSE)

Arguments

Value

A list with named elements: "bins", the bin separator values; "labeled", are the bins labeled?; "obs", binned observed data; "sim", binned simulated data. Additionally, "tmp_bins" is added for tte data.

Functions

• define_bins_tte(): Define bins for time-to-event data

Define data column defaults for various softwares

Description

Define data column defaults for various softwares

Usage

define_data_columns(sim, obs, sim_cols, obs_cols, software_type)

Arguments

Check and standardize limits of quantification

Description

Check and standardize limits of quantification

Usage

define_loq(
  lloq = NULL,
  uloq = NULL,
  pred_corr = FALSE,
  pred_corr_lower_bnd = 0,
  require_loq
)

Arguments

Value

A list with elements "lloq", "uloq", "cens_limit", "pred_corr", "pred_corr_lower_bnd", and "cens_type". "cens_limit" indicates the range for censoring (c(lloq, uloq), NULL if neither is given), "cens_type" indicates whether the data are "left" censored (low values are censored, only), "right" (high values are censored, only), "both" (low and high values are censored), or "neither" (no values are censored).

Draw parameters from multivariate distribution

Description

Draw parameters from multivariate distribution

Usage

draw_params_mvr(ids, n_sim, theta, omega_mat, par_names = NULL)

Arguments

Remove values that are not observed values from data

Description

Remove values that are not observed values from data

Usage

filter_dv(x, verbose = FALSE, ...)

Arguments

Value

x With non-observation rows removed

Prepare VPC data for future calculations by standardizing column names and modifying the input data based on the limits of quantification, stratification, and logarithmic values.

Description

Prepare VPC data for future calculations by standardizing column names and modifying the input data based on the limits of quantification, stratification, and logarithmic values.

Usage

format_vpc_input_data(
  dat,
  cols,
  lloq,
  uloq,
  stratify,
  log_y,
  log_y_min,
  what = "observed",
  verbose = FALSE,
  pred_corr = FALSE
)

format_vpc_input_data_tte(
  dat,
  cols,
  stratify,
  rtte,
  rtte_calc_diff,
  what = "observed",
  verbose = FALSE
)

Arguments

Value

dat modified based on other inputs.

A named list with "dat" modified, as required, and "stratify" with the stratification parameters

Functions

• format_vpc_input_data_tte(): Prepare VPC data for future calculations for time-to-event data

Create a ggplot for each vpcdb type

Description

These functions are not to be called directly by users; they are for internal use. Users should call plot_vpc().

Usage

ggplot.vpcdb_continuous(
  data = NULL,
  mapping = NULL,
  ...,
  environment = parent.frame()
)

ggplot.vpcdb_categorical(
  data = NULL,
  mapping = NULL,
  ...,
  environment = parent.frame()
)

ggplot.vpcdb_censored(
  data = NULL,
  mapping = NULL,
  ...,
  environment = parent.frame()
)

ggplot.vpcdb_time_to_event(
  data = NULL,
  mapping = NULL,
  ...,
  environment = parent.frame()
)

Arguments

Function copied from ggplot2:::label_variable, with addition of ' Copied here since CRAN does not like use of ':::'.

Description

Function copied from ggplot2:::label_variable, with addition of ' Copied here since CRAN does not like use of ':::'.

Usage

ggplot2_label_variable(labels, multi_line = TRUE)

Arguments

Custom ggplot2 labeller function.

Description

Slight rewrite of ggplot2::label_both, to make sure that labels for events are ordered appropriately when doing facet_wrap.

Usage

label_vpc_tte(labels, multi_line = TRUE, sep = ": ")

Arguments

Calculate fraction of observations below lloq / above uloq

Description

Calculate fraction of observations below lloq / above uloq

Usage

loq_frac(x, limit = 1, cens = c("left", "right", "neither", "both"))

Arguments

Value

The fraction of observations (NA is counted as below/above)

Create a customized VPC theme

Description

Create a customized VPC theme

Usage

new_vpc_theme(update = NULL)

Arguments

Details

This function creates a theme that customizes how the VPC looks, i.e. colors, fills, transparencies, linetypes, sizes, etc. The following arguments can be specified in the input list:

obs_color

color for observations points

obs_size

size for observation points

obs_median_color

color for median observation line

obs_median_linetype

linetype for median observation line

obs_median_linewidth

linewidth for median observation line

obs_ci_fill

color for observation CI fill

obs_ci_color

color for observation CI lines

obs_ci_linetype

linetype for observation CI lines

obs_ci_linewidth

linewidth for observations CI lines

sim_pi_fill

fill color for simulated prediction interval areas

sim_pi_alpha

transparency for simulated prediction interval areas

sim_pi_color

color for simulated prediction interval lines

sim_pi_linetype

linetype for simulated prediction interval lines

sim_pi_linewidth

linewidth for simulated prediction interval lines

sim_median_fill

fill color for simulated median area

sim_median_alpha

transparency for simulated median area

sim_median_color

color for simulated median line

sim_median_linetype

linetype for simulated median line

sim_median_linewidth

linewidth for simulated median line

bin_separators_color

color for bin separator lines, NA for don't plot

bin_separators_location

where to plot bin separators ("t" for top, "b" for bottom)

loq_color

color of line showing limit of quantification

Value

A list with vpc theme specifiers

Examples

theme1 <- new_vpc_theme(update = list(
  obs_color = "red",
  obs_ci_color = "#aa0000",
  obs_alpha = .3,
  sim_pi_fill = "#cc8833",
  sim_pi_linewidth = 2
))
vpc(simple_data$sim, simple_data$obs, vpc_theme = theme1)

Simulate PK data from a 1-compartment iv model

Description

Simulate PK data from a 1-compartment iv model

Usage

pk_iv_1cmt(
  t,
  t_inf = 1,
  tau = 24,
  dose = 120,
  CL = 0.345,
  Vc = 1.75,
  ruv = NULL
)

Arguments

Value

A vector of predicted values, with or without added residual variability

Examples

dat1 <- vpc:::pk_iv_1cmt(t = c(0:72), tau = 24, dose = 120,
                   CL = 5, Vc = 50)
dat2 <- vpc:::pk_iv_1cmt(t = c(0:72), tau = 24, dose = 120,
                   CL = 5, Vc = 50,
                   ruv = list(proportional = 0.1, additive = 0.1))

Simulate PK data from a 1-compartment oral model

Description

Simulate PK data from a 1-compartment oral model

Usage

pk_oral_1cmt(t, tau = 24, dose = 120, ka = 1, ke = 1, cl = 10, ruv = NULL)

Arguments

Value

A vector of predicted values, with or without added residual variability

Examples

dat1 <- vpc:::pk_oral_1cmt(t = c(0:72), tau = 24, dose = 120,
                     ka = 1, ke = 1, cl = 10)
dat2 <- vpc:::pk_oral_1cmt(t = c(0:72), tau = 24, dose = 120,
                     ka = 1, ke = 1, cl = 10,
                     ruv = list(proportional = 0.1, additive = 0.1))

VPC plotting function

Description

This function performs no parsing of data, it just plots the already calculated statistics generated using one of the 'vpc' functions.

Usage

plot_vpc(
  db,
  show = NULL,
  vpc_theme = NULL,
  smooth = TRUE,
  log_x = FALSE,
  log_y = FALSE,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  verbose = FALSE
)

Arguments

See Also

vpc_cens, vpc_tte, vpc_cat

Examples

## See vpc.ronkeizer.com for more documentation and examples

library(vpc)
vpc_db <- vpc(sim = simple_data$sim, obs = simple_data$obs, vpcdb = TRUE)
plot_vpc(vpc_db, title = "My new vpc", x = "Custom x label")

Helper function to simplify vpc plotting (not intended to be called directly)

Description

Helper function to simplify vpc plotting (not intended to be called directly)

Usage

plot_vpc_helper(
  db,
  show = NULL,
  vpc_theme = NULL,
  smooth = TRUE,
  log_x = FALSE,
  log_y = FALSE,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  verbose = FALSE
)

## Default S3 method:
plot_vpc_helper(db, ...)

## S3 method for class 'vpcdb_continuous'
plot_vpc_helper(
  db,
  ...,
  show = NULL,
  vpc_theme = NULL,
  smooth = TRUE,
  log_x = FALSE,
  log_y = FALSE,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  verbose = FALSE
)

## S3 method for class 'vpcdb_categorical'
plot_vpc_helper(db, ...)

## S3 method for class 'vpcdb_censored'
plot_vpc_helper(db, ...)

## S3 method for class 'vpcdb_time_to_event'
plot_vpc_helper(
  db,
  ...,
  show = NULL,
  vpc_theme = NULL,
  smooth = TRUE,
  log_x = FALSE,
  log_y = FALSE,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  verbose = FALSE
)

Arguments

Core prediction correction function

Description

Perform pred-correction for predictions that were non-zero and were not missing observation. The latter can happen e.g. when censored data is set to NA in 'format_vpc_input_data()'.

Usage

pred_correction_core(data, pred_col, pred_corr_lower_bnd)

Arguments

Value

data.frame

Calculate quantiles respecting the censored data

Description

Calculate quantiles respecting the censored data

Usage

quantile_cens(
  x,
  probs = 0.5,
  limit = 1,
  cens = c("left", "right", "neither", "both")
)

Arguments

Value

The quantile of x treating NA values as censored

NONMEM output table import function

Description

Quickly import NONMEM output tables into R. Function taken from 'modelviz' package by Benjamin Guiastrennec. When both skip and header are NULL, read_nmtab will automatically detect the optimal settings to import the tables. When more than one files are provided for a same NONMEM run, they will be combined into a single data.frame.

Usage

read_table_nm(
  file = NULL,
  skip = NULL,
  header = NULL,
  rm_duplicates = FALSE,
  nonmem_tab = TRUE
)

Arguments

Value

A data.frame

Examples

## Not run: 
data <- read_table_nm(file = '../models/pk/sdtab101')

## End(Not run)

Read in VPC data

Description

Read in VPC data

Usage

read_vpc(sim, obs, psn_folder, software, sim_cols, obs_cols, verbose = FALSE)

Arguments

Value

A list with names of "sim", "obs", "software", and "cols"

Replace list elements by name

Description

Replace list elements by name

Usage

replace_list_elements(list, replacement)

Arguments

Details

Finds and replaces list elements by name and throws an error if an element is not available in the original list. This is a local duplicate of the PKPDmisc copy for the VPC package to reduce dependency on PKPDmisc at this time.

Examples

## Not run: 
list <- list(ipred = "ipred", dv = "dv", idv = "idv", "pred" = "pred")
replacement <- list(dv = "conc", idv = "time")
list <- replace_list_elements(list, replacement)

## End(Not run)

Simulated RTTE data (1x)

Description

An example dataset with simulated repeated time-to-event data

Usage

rtte_obs_nm

Format

An object of class data.frame with 573 rows and 6 columns.

Simulated RTTE data (100x)

Description

An example dataset with simulated repeated time-to-event data (100 simulations)

Usage

rtte_sim_nm

Format

An object of class data.frame with 2000000 rows and 7 columns.

Defaults for show argument with various endpoint types

Description

Defaults for show argument with various endpoint types

Usage

show_default

Format

An object of class list of length 4.

A small rich dataset

Description

A small rich dataset

Usage

simple_data

Format

An object of class list of length 2.

Details

a list containing the obs and sim data for an example dataset to run a simple vpc.

Examples

## Not run: 
vpc(simple_data$sim, simple_data$obs)

## End(Not run)

Confirm that a column is in the data, and rename data to prepare that column for later use.

Description

Confirm that a column is in the data, and rename data to prepare that column for later use.

Usage

standardize_column(dat, cols, colname, coldesc, what, default)

Arguments

Value

If colname is already named colname in dat, dat unchanged. If not, check if dat has that column name already, and if so, name the existing dat[[colname]] to dat[[paste0(colname, ".old")]] and then rename cols[[colname]] to colname.

Empty ggplot2 theme

Description

Empty ggplot2 theme

Usage

theme_empty()

Examples

vpc(simple_data$sim, simple_data$obs) + theme_empty()

A nicer default theme for ggplot2

Description

A nicer default theme for ggplot2

Usage

theme_plain()

Examples

vpc(simple_data$sim, simple_data$obs) + theme_plain()

Lower to full triangle

Description

Convert the lower triangle of a covariance matrix to a full matrix object

Usage

triangle_to_full(vect)

Arguments

VPC function

Description

Creates a VPC plot from observed and simulation data

Usage

vpc(sim, ...)

## Default S3 method:
vpc(sim, ...)

vpc_vpc(
  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,
  stratify = NULL,
  pred_corr = FALSE,
  pred_corr_lower_bnd = 0,
  pi = c(0.05, 0.95),
  ci = c(0.05, 0.95),
  uloq = NULL,
  lloq = NULL,
  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,
  ...
)

Arguments

Value

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

See Also

vpc_cens, vpc_tte, vpc_cat

Examples

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

# Basic commands:
vpc(sim = simple_data$sim, obs = simple_data$obs)
vpc(sim = simple_data$sim, obs = simple_data$obs, lloq = 20)

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
)

Arguments

Value

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

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)

VPC function for left- or right-censored data (e.g. BLOQ data)

Description

Creates a VPC plot from observed and simulation data for censored data. Function can handle both left- (below lower limit of quantification) and right-censored (above upper limit of quantification) data.

Usage

vpc_cens(
  sim = NULL,
  obs = NULL,
  psn_folder = NULL,
  bins = "jenks",
  n_bins = 8,
  bin_mid = "mean",
  obs_cols = NULL,
  sim_cols = NULL,
  software = "auto",
  show = NULL,
  stratify = NULL,
  stratify_color = NULL,
  ci = c(0.05, 0.95),
  uloq = NULL,
  lloq = NULL,
  xlab = "Time",
  ylab = "Probability of <LOQ",
  title = NULL,
  smooth = TRUE,
  vpc_theme = NULL,
  facet = "wrap",
  labeller = NULL,
  vpcdb = FALSE,
  verbose = FALSE
)

Arguments

Value

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

See Also

vpc, vpc_tte, vpc_cat

Examples

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

vpc_cens(sim = simple_data$sim, obs = simple_data$obs, lloq = 30)
vpc_cens(sim = simple_data$sim, obs = simple_data$obs, uloq = 120)

A collection of internal ggplot helpers for VPC plotting

Description

A collection of internal ggplot helpers for VPC plotting

Usage

geom_bin_sep(bins, show, vpc_theme)

geom_hline_loq(data, vpc_theme)

geom_obs_cens_dat_tte(data)

geom_obs_ci_continuous(data, show, vpc_theme)

geom_obs_ci_tte(data, show, vpc_theme)

geom_obs_dv_continuous(data, show, vpc_theme)

geom_obs_km(data)

geom_obs_median_continuous(data, show, vpc_theme)

geom_sim_km(data, show)

geom_sim_median_continuous(data, show, vpc_theme)

geom_sim_median_tte(data, show, smooth)

geom_sim_median_ci_continuous(data, show, smooth, vpc_theme)

geom_sim_pi_as_area_continuous(data, show, smooth, vpc_theme)

geom_sim_pi_as_area_tte(data, show, smooth, vpc_theme)

geom_sim_pi_continuous(data, show, vpc_theme)

geom_sim_pi_ci_continuous(data, show, smooth, vpc_theme, what = c("q5", "q95"))

facet_continuous(data)

facet_tte(data)

guides_stratify_color(data)

scale_x_log10_vpc(data, show)

scale_y_log10_vpc(show)

Arguments

Functions

• geom_bin_sep(): Show rug plot of bin separators

• geom_hline_loq(): Generate hlines for the lloq/uloq

• geom_obs_cens_dat_tte(): Show the cens_dat for time-to-events

• geom_obs_ci_continuous(): Show confidence interval for observed, continuous data

• geom_obs_ci_tte(): Show confidence interval for observed, time-to-event data

• geom_obs_dv_continuous(): Show observed data points

• geom_obs_km(): Show observed Kaplan-Meier line

• geom_obs_median_continuous(): Show observed median line for continuous data

• geom_sim_km(): Show simulated Kaplan-Meier curves for time-to-event data

• geom_sim_median_continuous(): Show simulated median line for continuous data

• geom_sim_median_tte(): Show simulated median line (or steps) for time-to-event data

• geom_sim_median_ci_continuous(): Show simulated median confidence interval for continuous data

• geom_sim_pi_as_area_continuous(): Show simulated prediction interval area for continuous data

• geom_sim_pi_as_area_tte(): Show simulated prediction interval area for time-to-event data

• geom_sim_pi_continuous(): Show simulated prediction interval lines for continuous data

• geom_sim_pi_ci_continuous(): Show simulated prediction interval confidence interval areas for continuous data

• facet_continuous(): Facet continuous data by stratification factors

• facet_tte(): Facet time-to-event data by stratification factors

• guides_stratify_color(): Generate guides for stratification fill and colour

• scale_x_log10_vpc(): Optionally show log-x scale

• scale_y_log10_vpc(): Optionally show log-y scale

VPC function for time-to-event (survival) data

Description

This function can be used for either single time-to-event (TTE) or repeated time-to-event (RTTE) data.

Usage

vpc_tte(
  sim = NULL,
  obs = NULL,
  psn_folder = NULL,
  rtte = FALSE,
  rtte_calc_diff = TRUE,
  rtte_conditional = TRUE,
  events = NULL,
  bins = FALSE,
  n_bins = 10,
  software = "auto",
  obs_cols = NULL,
  sim_cols = NULL,
  kmmc = NULL,
  reverse_prob = FALSE,
  stratify = NULL,
  stratify_color = NULL,
  ci = c(0.05, 0.95),
  xlab = "Time",
  ylab = "Survival (%)",
  show = NULL,
  as_percentage = TRUE,
  title = NULL,
  smooth = FALSE,
  vpc_theme = NULL,
  facet = "wrap",
  labeller = NULL,
  verbose = FALSE,
  vpcdb = FALSE
)

Arguments

Details

Creates a VPC plot from observed and simulation survival data

Value

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

See Also

vpc, vpc_tte, vpc_cens

Examples

## See vpc-docs.ronkeizer.com for more documentation and examples.

## Example for repeated) time-to-event data
## with NONMEM-like data (e.g. simulated using a dense grid)

data(rtte_obs_nm)
data(rtte_sim_nm)

# treat RTTE as TTE, no stratification
vpc_tte(sim = rtte_sim_nm[rtte_sim_nm$sim <= 20,],
       obs = rtte_obs_nm,
       rtte = FALSE,
       sim_cols=list(dv = "dv", idv = "t"), obs_cols=list(idv = "t"))