VPC based on ui model
Usage
vpcPlot(
fit,
data = NULL,
n = 300,
bins = "jenks",
n_bins = "auto",
bin_mid = "mean",
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 = fit$dataUloq,
lloq = fit$dataLloq,
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,
...,
seed = 1009,
idv = "time",
cens = FALSE
)
Arguments
- fit
nlmixr2 fit object
- data
this is the data to use to augment the VPC fit. By default is the fitted data, (can be retrieved by
getData
), but it can be changed by specifying this argument.- n
Number of VPC simulations. By default 100
- 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.
- 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)
- ...
Additional arguments passed to
nlmixr2plot::vpcPlot()
.- seed
an object specifying if and how the random number generator should be initialized
- idv
Name of independent variable. For
vpcPlot()
andvpcCens()
the default is"time"
forvpcPlotTad()
andvpcCensTad()
this is"tad"
- cens
is a boolean to show if this is a censoring plot or not. When
cens=TRUE
this is actually a censoring vpc plot (withvpcCens()
andvpcCensTad()
). Whencens=FALSE
this is traditional VPC plot (vpcPlot()
andvpcPlotTad()
).
Examples
# \donttest{
one.cmt <- function() {
ini({
## You may label each parameter with a comment
tka <- 0.45 # Log Ka
tcl <- log(c(0, 2.7, 100)) # Log Cl
## This works with interactive models
## You may also label the preceding line with label("label text")
tv <- 3.45; label("log V")
## the label("Label name") works with all models
eta.ka ~ 0.6
eta.cl ~ 0.3
eta.v ~ 0.1
add.sd <- 0.7
})
model({
ka <- exp(tka + eta.ka)
cl <- exp(tcl + eta.cl)
v <- exp(tv + eta.v)
linCmt() ~ add(add.sd)
})
}
fit <- nlmixr2est::nlmixr(one.cmt, nlmixr2data::theo_sd, est="focei")
#>
#>
#>
#>
#> ℹ parameter labels from comments will be replaced by 'label()'
#> → Calculating residuals/tables
#> ✔ done
#> → compress origData in nlmixr2 object, save 5952
#> → compress parHist in nlmixr2 object, save 2040
vpcPlot(fit)
#>
#>
# }