This function draws the visual predictive check (VPC) plot using additive quantile regression. The quantile regression methods are used to calculate quantiles.
aqrVPC(orig_data,
sim_data,
probs = c(0.1,0.5,0.9),
conf.level = 0.95,
X_name = "TIME",
Y_name = "DV",
MissingDV = NULL,
plot_caption = TRUE,
DV_point = TRUE,
plot_flag = TRUE,
linesize = 0.7,
pointsize = 0.7,
captionsize = 10,
qss_lambda = NULL, ...)
aqrVPC plot or the values to draw aqrVPC plot.
A data frame of original data with X and Y variable.
A matrix of simulated data with only Y values collected.
A numeric vector of probabilities.
Confidence level of the interval.
Name of X variable in orig_data (usually "TIME" in pharmacokinetic data).
Name of Y variable in orig_data (usually "DV" in pharmacokinetic data).
Name of missing indicator variable in orig_data, which have value 1 if missing, value 0 otherwise. (usually "MDV" in pharmacokinetic data).
Put caption with additional information if TRUE; omit if FALSE.
Draw point (X, Y) in the plot if TRUE; omit if FALSE.
Draw plot if TRUE; generate data for drawing plot if FALSE.
Size of line in the plot.
Size of point in the plot.
Size of caption.
Smoothing parameter in quantreg::qss function. Larger lambda produces a smoother fit.
Arguments to be passed to methods.
Koenker, Roger, and Kevin F. Hallock. "Quantile regression." Journal of economic perspectives 15.4 (2001): 143-156.
Jamsen, K. M., Patel, K., Nieforth, K., & Kirkpatrick, C. M. (2018). A regression approach to visual predictive checks for population pharmacometric models. CPT: pharmacometrics & systems pharmacology, 7(10), 678-686.
# \donttest{
data(origdata)
data(simdata)
aqrVPC(origdata,simdata)
# }
Run the code above in your browser using DataLab