View source: R/dynrGetDerivs.R

plotGCV | 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

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

`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) |

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))

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.

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