zeitzeiger: Train and test a ZeitZeiger predictor In jakejh/zeitzeiger: Regularized supervised learning for high-dimensional data from an oscillatory system

Description

`zeitzeiger` sequentially calls `zeitzeigerFit`, `zeitzeigerSpc`, and `zeitzeigerPredict`.

Usage

 ```1 2 3``` ```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 matrix for which the SPCs will be calculated. `useSpc` Logical indicating whether to use `SPC` (default) or `svd`. `sumabsv` L1-constraint on the SPCs, passed to `SPC`. `orth` Logical indicating whether to require left singular vectors be orthogonal to each other, passed to `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` Result from `zeitzeigerFit` `spcResult` Result from `zeitzeigerSpc` `predResult` Result from `zeitzeigerPredict`

`zeitzeigerFit`, `zeitzeigerSpc`, `zeitzeigerPredict`