vpc: VPC function
In vpc: Create Visual Predictive Checks

Description Usage Arguments Value See Also Examples

Creates a VPC plot from observed and simulation data

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,
  ...
)

`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
`...`	Other arguments sent to other methods (like xpose or nlmixr); Note these arguments are not used in the default vpc and are ignored by the default method.
`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`	observation dataset column names (list elements: "dv", "idv", "id", "pred")
`sim_cols`	simulation dataset column names (list elements: "dv", "idv", "id", "pred", "sim")
`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)
`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)

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

sim_data, vpc_cens, vpc_tte, vpc_cat

## 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)