quantVPC | R Documentation |
The quantified visual predictive check visually represents actual and unavailable observations around predicted medians, regardless of the density or shape of the observed data distribution, through the form of a percent.
quantVPC(orig_data, sim_data, N_xbin = NULL, prob = 0.5, X_name = "TIME", Y_name = "DV", MissingDV = NULL, Kmethod = "cluster", maxK = NULL, beta = 0.2, lambda = 0.3, R = 4, C1 = 2.5, C2 = 7.8, ...)
orig_data |
A data frame of original data with X and Y variable. |
sim_data |
A matrix of simulated data with only Y values collected. |
N_xbin |
Number of bins in X variable. If NULL, optimal number of bins are automatically calcuated using optK function. |
prob |
Scalar of probability. |
X_name |
Name of X variable in orig_data (usually "TIME" in pharmacokinetic data). |
Y_name |
Name of Y variable in orig_data (usually "DV" in pharmacokinetic data). |
MissingDV |
Name of missing indicator variable in orig_data, which have value 1 if missing, value 0 otherwise. (usually "MDV" in pharmacokinetic data). |
Kmethod |
The way to calculate the penalty in automatic binning."cluster" or "kernel". |
maxK |
The maximum number of bins. |
beta |
Additional parameter for automatic binning, used in optK function. |
lambda |
Additional parameter for automatic binning, used in optK function. |
R |
Additional parameter for automatic binning, used in optK function. |
C1 |
Additional parameter for automatic binning, used in optK function. |
C2 |
Additional parameter for automatic binning, used in optK function. |
... |
Arguments to be passed to methods. |
quantVPC plot
Post, T.M., et al. (2008) Extensions to the visual predictive check for facilitate model performance evaluation, Journal of pharmacokinetics and pharmacodynamics, 35(2), 185-202
data(origdata) data(simdata) quantVPC(origdata,simdata,prob=0.5,N_xbin=8)
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