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]