loocv: K fold CV for O2PLS

Description

For (a grid of) values for a, nx and ny, loocv estimates the prediction error using k-fold CV.

Usage

loocv(
  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.).

The parameters a, a2 and b2 can be integers or vectors of integers. A for loop is used to loop over all combinations. The resulting output is a list, which is more easy to interpret if you use array(unlist(output_of_loocv$CVerr)) as in the example below. The array wil have varying a along the first dimension and a2 and b2 along the second and third respectively. Typing example(loocv) (hopefully) clarifies this function.