Predict corresponding time for observations on cross-validation

Make predictions for each observation for each fold of cross-validation.

Usage

zeitzeigerPredictCv(
  x,
  time,
  foldid,
  spcResultList,
  nKnots = 3,
  nSpc = NA,
  timeRange = seq(0, 1 - 0.01, 0.01),
  dopar = TRUE
)

x: Matrix of measurements, observations in rows and features in columns.
time: Vector of values of the periodic variable for observations, where 0 corresponds to the lowest possible value and 1 corresponds to the highest possible value.
foldid: Vector of values indicating the fold to which each observation belongs.
spcResultList: Output of zeitzeigerSpcCv().
nKnots: Number of internal knots to use for the periodic smoothing spline.
nSpc: Vector of the number of SPCs to use for prediction. If NA (default), nSpc will become 1:K, where K is the number of SPCs in spcResult. Each value in nSpc will correspond to one prediction for each test observation. A value of 2 means that the prediction will be based on the first 2 SPCs.
timeRange: Vector of values of the periodic variable at which to calculate likelihood. The time with the highest likelihood is used as the initial value for the MLE optimizer.
dopar: Logical indicating whether to process the folds in parallel. Use doParallel::registerDoParallel() to register the parallel backend.

A list of the same structure as zeitzeigerPredict(), combining the results from each fold of cross-validation.

timeDepLike: 3-D array of likelihood, with dimensions for each observation, each element of nSpc, and each element of timeRange.
mleFit: List (for each element in nSpc) of lists (for each observation) of mle2 objects.
timePred: Matrix of predicted times for observations by values of nSpc.