cvgrid: cross validation grid search for l1-penalized additive mixed...
In bdsegal/psplinesl1: P-splines with an l_1 penalty for repeated measures

This function runs cross-validation grid search to estimate the smoothing parameters

cvgrid(y, X, rand, id, data, K = 5, se1 = FALSE, smoothInit = NULL,
  pathLength = 20, useTestRanEff = TRUE, tauMax = NULL, revPath = FALSE,
  paramOrder = NULL, epsilonAbs = 1e-04, epsilonRel = 1e-04,
  iterMax = 1000, centerZ = FALSE, verbose = 2, fileName = NULL)

`y`	character string denoting output variable
`X`	list of fixed effect design matrices setup by l1smooth
`rand`	list of random effect terms setup by randomEffects
`id`	character string denoting subject id variable
`data`	dataset
`se1`	TRUE/FALSE if true, sets smoothing parameter to value with maximum CV error within 1 sd of the value that minimizes CV error
`smoothInit`	vector of initial smoothing parameter values
`pathLength`	length of each smoothing parameter path (evenly spaced on the log scale)
`useTestRanEff`	TRUE/FALSE if true, estimates random effects with test sample
`tauMax`	scalar max value to use in tau path. If NULL, set to maximum value used for fixed effect smoothing parameters.
`revPath`	TRUE/FALSE if true, estimates smoothing paths from smallest to largest
`paramOrder`	order of smoothing parameters to be fit
`epsilonAbs`	absolute error in ADMM (tolerance for convergence)
`epsilonRel`	relative error in ADMM (tolerance for convergence)
`iterMax`	maximum number of ADMM iterations
`centerZ`	TRUE/FALSE If True, imposes centering constraints on random effects
`verbose`	integer (0, 1, 2) amount of information printed to terminal. 0 = no information, 1 = path iteration, 2 = path iteration + fold results
`saveFile`	file name for saving CV results as csv file, e.g. "CV_results". Do not include extension. If NULL, no file saved.

library(psplinesl1)
data(simData)

# setup p-spline matrices
X <- list(ps(x = "x", data = simData, 
             norder = 2, k = 1, width = 0.05,
             center = TRUE))

# setup random effect matrices
rand <- re(x = "x", id = "id", data = simData,
           randomCurves = FALSE)

# run cross validation and view results
cvOut <- cvgrid(y = "y", id = "id", X = X, rand = rand,
                  K = 5,
                  pathLength = 20,
                  data = simData)
cvOut

# fit model with all data
a1 <- admm(y = "y", X, Z = rand$Z, S = rand$S,
            tau = cvOut$smoothOpt[1],
            lambda = cvOut$smoothOpt[2:(length(X)+1)],
            rho = min(5, max(cvOut$smoothOpt)),
            data = simData)

# get and plot fitted model with confidence bands
CI <- ci(model = a1, alpha = 0.05)
plot(CI)

# extract values from ci object for custom plotting
CIpoly <- data.frame(x = c(CI[[1]]$x, rev(CI[[1]]$x)), 
                     y = c(CI[[1]]$lower, rev(CI[[1]]$upper)))

ggplot(aes(x = x, y = y), data = newDat)+
  geom_polygon(data = CIpoly, fill = "grey")+
  geom_line(aes(x = CI[[1]]$x, y = CI[[1]]$smooth))