bootVPC {nlmeVPC}

The bootstrap visual predictive checks.

Description

This function draws the visual predictive check plot with bootstrapped data. It compares the distribution of the simulated data to the distribution of the bootstrap samples that draw from the observed data. This plot reflects the uncertainty of the observed data and allows for more objective comparisons with the predicted median.

Usage

bootVPC(orig_data,
        sim_data,
        B = 1000,                  
        N_xbin = NULL,
        conf.level = 0.95,
        X_name = "TIME",
        Y_name = "DV",
        subject_name = "ID",                  
        MissingDV = NULL,
        DV_point = TRUE,                  
        plot_caption = TRUE,                  
        plot_flag = TRUE,
        linesize = 0.7,
        pointsize = 0.7,
        Kmethod = "cluster",                
        maxK = NULL,
        beta = 0.2,
        lambda = 0.3,
        R = 4,                
        C1 = 2.5,
        C2 = 7.8, ...)

Arguments

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.

B

Number of bootstrap samples.

N_xbin

Number of bins in X variable. If NULL, optimal number of bins are automatically calcuated using optK function.

conf.level

Confidence level of the interval.

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).

subject_name

Name of subject variable in orig_data (usually "ID" 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).

DV_point

Draw point (X, Y) in the plot if TRUE; omit if FALSE.

plot_caption

Put caption with additional information if TRUE; omit if FALSE.

plot_flag

Draw plot if TRUE; generate data for drawing plot if FALSE.

linesize

Size of line in the plot.

pointsize

Size of point in the plot.

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.

Value

bootVPC plot or the values to draw bootVPC plot.

References

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

Examples

data(origdata)
data(simdata)
bootVPC(origdata,simdata,N_xbin=8)