plotGCV: A function to evaluate the generalized cross-validation (GCV)...

In dynr: Dynamic Models with Regime-Switching

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.

dynr documentation built on May 29, 2024, 2:49 a.m.