Train and test a ZeitZeiger predictor

Train and test a ZeitZeiger predictor, calling the necessary functions.

zeitzeiger(
  xTrain,
  timeTrain,
  xTest,
  nKnots = 3,
  nTime = 10,
  useSpc = TRUE,
  sumabsv = 2,
  orth = TRUE,
  nSpc = 2,
  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.
nKnots	Number of internal knots to use for the periodic smoothing spline.
nTime	Number of time-points by which to discretize the time-dependent behavior of each feature. Corresponds to the number of rows in the matri for which the SPCs will be calculated.
useSpc	Logical indicating whether to use `PMA::SPC()` (default) or `base::svd()`.
sumabsv	L1-constraint on the SPCs, passed to `PMA::SPC()`.
orth	Logical indicating whether to require left singular vectors be orthogonal to each other, passed to `PMA::SPC()`.
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

fitResult

Output of zeitzeigerFit()

spcResult

Output of zeitzeigerSpc()

predResult

Output of zeitzeigerPredict()

Arguments

Value

See also