vpcPlot | R Documentation |
VPC based on ui model
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 ) vpcPlotTad(..., idv = "tad") vpcCensTad(..., cens = TRUE, idv = "tad") vpcCens(..., cens = TRUE, idv = "time")
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
|
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) |
... |
Args sent to |
seed |
an object specifying if and how the random number generator should be initialized |
idv |
Name of independent variable. For |
cens |
is a boolean to show if this is a censoring plot or
not. When |
Simulated dataset (invisibly)
Matthew L. Fidler
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") vpcPlot(fit)
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