plotGCV {dynr}R Documentation

A function to evaluate the generalized cross-validation (GCV) values associated with derivative estimates via Bsplines at a range of specified smoothing parameter (lambda) values

Description

A function to evaluate the generalized cross-validation (GCV) values associated with derivative estimates via Bsplines at a range of specified smoothing parameter (lambda) values

Usage

plotGCV(theTimes, norder, roughPenaltyMax, dataMatrix, lowLambda, upLambda,
  lambdaInt, isPlot)

Arguments

theTimes

The time points at which derivative estimation are requested

norder

Order of Bsplines - usually 2 higher than roughPenaltyMax

roughPenaltyMax

Penalization order. Usually set to 2 higher than the highest-order derivatives desired

dataMatrix

Data of size total number of time points x total number of subjects

lowLambda

Lower limit of lambda values to be tested. Here, lambda is a positive smoothing parameter, with larger values resulting in greater smoothing)

upLambda

Upper limit of lambda

lambdaInt

The interval of lambda values to be tested.

isPlot

A binary flag on whether to plot the gcv values (0 = no, 1 = yes)

Value

A data frame containing: 1. lambda values; 2. edf (effective degrees of freedom); 3. GCV (Generalized cross-validation value as averaged across units (e.g., subjects))

References

Chow, S-M. (2019). Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models. Multivariate Behavioral Research. https://www.nihms.nih.gov/pmc/articlerender.fcgi?artid=1520409

Chow, S-M., *Bendezu, J. J., Cole, P. M., & Ram, N. (2016). A Comparison of Two- Stage Approaches for Fitting Nonlinear Ordinary Differential Equation (ODE) Models with Mixed Effects. Multivariate Behavioral Research, 51, 154-184. Doi: 10.1080/00273171.2015.1123138.

Examples

#outMatrix = plotGCV(theTimes,norder,roughPenaltyMax,out2,lambdaLow, 
#lambdaHi,lambdaBy,isPlot)

[Package dynr version 0.1.16-27 Index]