Predict corresponding time for test observations

Predict the value of the periodic variable for test observations given training data and SPCs.

zeitzeigerPredict(
  xTrain,
  timeTrain,
  xTest,
  spcResult,
  nKnots = 3,
  nSpc = NA,
  timeRange = seq(0, 1 - 0.01, 0.01)
)

Arguments

xTrain	Matrix of measurements for training data, observations in rows and features in columns.
timeTrain	Vector of values of the periodic variable for training observations, where 0 corresponds to the lowest possible value and 1 corresponds to the highest possible value.
xTest	Matrix of measurements for test data, observations in rows and features in columns.
spcResult	Output of `zeitzeigerSpc()`.
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.

timeDepLike

3-D array of likelihood, with dimensions for each test observation, each element of nSpc, and each element of timeRange.

mleFit

List (for each element in nSpc) of lists (for each test observation) of mle2 objects.

timePred

Matrix of predicted times for test observations by values of nSpc.