loocv_combi: K-fold CV based on symmetrized prediction error

Description

The prediction error of both X~Xhat and Y~Yhat are summed. This provides a symmetrized version of loocv.

Usage

loocv_combi(
  X,
  Y,
  a = 1:2,
  a2 = 1,
  b2 = 1,
  fitted_model = NULL,
  func = o2m,
  app_err = F,
  kcv,
  stripped = TRUE,
  p_thresh = 3000,
  q_thresh = p_thresh,
  tol = 1e-10,
  max_iterations = 100,
  seed = "off"
)

Value

List with two numeric vectors:

CVerr: Contains the k-fold CV estimated RMSEP
Fiterr: Contains the apparent error

Arguments

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 integers. Contains the numbers of joint components.
a2: Vector of integers. Contains the numbers of orthogonal components in \(X\).
b2: Vector of integers. Contains the numbers of orthogonal components in \(Y\).
fitted_model: List. Deprecated. O2PLS model fit with o2m. Is used to calculate the apparent error without recalculating this fit.
func: Function to fit the O2PLS model with. Only o2m and o2m_stripped are supported.
app_err: Logical. Deprecated. Should the apparent error also be computed?
kcv: Integer. The value of \(k\), i.e. the number of folds. Choose \(N\) for LOO-CV.
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.
seed: Integer. A random seed to make the analysis reproducible.

Details

Note that this function can be easily parallelized (on Windows e.g. with the parallel package.). If there are NAs in the CVerr component, this is due to an error in the fitting.