Predict corresponding time for observations on cross-validation

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

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

Arguments

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.