Predict corresponding time for observations on cross-validationSource:
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 )
Matrix of measurements, observations in rows and features in columns.
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.
Vector of values indicating the fold to which each observation belongs.
Number of internal knots to use for the periodic smoothing spline.
Vector of the number of SPCs to use for prediction. If
Kis the number of SPCs in
spcResult. Each value in
nSpcwill correspond to one prediction for each test observation. A value of 2 means that the prediction will be based on the first 2 SPCs.
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.
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.
3-D array of likelihood, with dimensions for each observation, each element of
nSpc, and each element of
List (for each element in
nSpc) of lists (for each observation) of
Matrix of predicted times for observations by values of