plotGCV function

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

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)

Returns

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.