Predict corresponding time for test observations

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

Usage

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

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.