crossval_o2m: Cross-validate procedure for O2PLS
In selbouhaddani/O2PLS: O2PLS: Two-Way Orthogonal Partial Least Squares

Description Usage Arguments Details Value Examples

Cross-validate procedure for O2PLS

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crossval_o2m(X, Y, a, ax, ay, nr_folds, nr_cores = 1, stripped = TRUE,
  p_thresh = 3000, q_thresh = p_thresh, tol = 1e-10,
  max_iterations = 100)

`X`	Numeric matrix. Vectors will be coerced to matrix with `as.matrix` (if this is possible)
`Y`	Numeric matrix. Vectors will be coerced to matrix with `as.matrix` (if this is possible)
`a`	Vector of positive integers. Denotes the numbers of joint components to consider.
`ax`	Vector of non-negative integers. Denotes the numbers of X-specific components to consider.
`ay`	Vector of non-negative integers. Denotes the numbers of Y-specific components to consider.
`nr_folds`	Positive integer. Number of folds to consider. Note: `kcv=N` gives leave-one-out CV. Note that CV with less than two folds does not make sense.
`nr_cores`	Positive integer. Number of cores to use for CV. You might want to use `detectCores()`. Defaults to 1.
`stripped`	Logical. Use the stripped version of o2m (usually when cross-validating)?
`p_thresh`	Integer. If `X` has more than `p_thresh` columns, a power method optimization is used, see `o2m2`
`q_thresh`	Integer. If `Y` has more than `q_thresh` columns, a power method optimization is used, see `o2m2`
`tol`	double. Threshold for power method iteration
`max_iterations`	Integer, Maximum number of iterations for power method

This is the standard CV approach. It minimizes the sum of the prediction errors of X and Y over a three-dimensional grid of integers. Parallelization is possible on all platforms. On Windows it uses makePSOCKcluster, then exports all necessary objects to the workers, and then calls parLapply. On OSX and Linux the more friendly mclapply is used, which uses forking. A print method is defined, printing the minimizers and minimum in a readible way. Also the elapsed time is tracked and reported.

List of class "cvo2m" with the original and sorted Prediction errors and the number of folds used.

local({
X = scale(jitter(tcrossprod(rnorm(100),runif(10))))
Y = scale(jitter(tcrossprod(rnorm(100),runif(10))))
crossval_o2m(X, Y, a = 1:4, ax = 1:2, ay = 1:2, 
             nr_folds = 5, nr_cores = 1)
})