VPC function
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)
## 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)
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