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)
)

## 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.

## Value

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.

zeitzeigerFit(), zeitzeigerSpc()