crossval_o2m_adjR2: Adjusted Cross-validate procedure for O2PLS
In OmicsPLS: Data Integration with Two-Way Orthogonal Partial Least Squares

Description Usage Arguments Details Value Examples

Combines CV with R2 optimization

crossval_o2m_adjR2(
  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 which the NIPALS method is deemed converged. Must be positive.
`max_iterations`	Integer. Maximum number of iterations for the NIPALS method.

This is an alternative way of cross-validating. It is proposed in citation(OmicsPLS). This approach is (much) faster than the standard crossval_o2m approach and works fine even with two folds. For each element in n it looks for nx and ny that maximize the R^2 between T and U in the O2PLS model. This approach often yields similar integer as the standard approach. We however suggest to use the standard approach to minimize the prediction error around the found integers.

data.frame with four columns: MSE, n, nx and ny. Each row corresponds to an element in a.

local({
X = scale(jitter(tcrossprod(rnorm(100),runif(10))))
Y = scale(jitter(tcrossprod(rnorm(100),runif(10))))
crossval_o2m_adjR2(X, Y, a = 1:4, ax = 1:2, ay = 1:2,
             nr_folds = 5, nr_cores = 1)
})