bigPLSR (version 0.7.2)

pls_fit: Unified PLS fit with auto backend and selectable algorithm

Description

Dispatches to a dense (Arm/BLAS) backend for in-memory matrices or to a streaming big.matrix backend when X (or Y) is a big.matrix. Algorithm can be chosen between: "simpls" (default), "nipals", "kernelpls", "widekernelpls", "rkhs" (Rosipal & Trejo), "klogitpls", "sparse_kpls", "rkhs_xy" (double RKHS), and "kf_pls" (Kalman-filter PLS, streaming).

The "kernelpls" paths now include a streaming XX' variant for big.matrix inputs, with an optional row-chunking loop controlled by chunk_cols.

Usage

pls_fit(
  X,
  y,
  ncomp,
  tol = 1e-08,
  backend = c("auto", "arma", "bigmem"),
  mode = c("auto", "pls1", "pls2"),
  algorithm = c("auto", "simpls", "nipals", "kernelpls", "widekernelpls", "rkhs",
    "klogitpls", "sparse_kpls", "rkhs_xy", "kf_pls"),
  scores = c("none", "r", "big"),
  chunk_size = 10000L,
  chunk_cols = NULL,
  scores_name = "scores",
  scores_target = c("auto", "new", "existing"),
  scores_bm = NULL,
  scores_backingfile = NULL,
  scores_backingpath = NULL,
  scores_descriptorfile = NULL,
  scores_colnames = NULL,
  return_scores_descriptor = FALSE,
  coef_threshold = NULL,
  kernel = c("linear", "rbf", "poly", "sigmoid"),
  gamma = 1,
  degree = 3L,
  coef0 = 0,
  approx = c("none", "nystrom", "rff"),
  approx_rank = NULL,
  class_weights = NULL
)

Value

a list with coefficients, intercept, weights, loadings, means, and optionally $scores.

Arguments

X

numeric matrix or bigmemory::big.matrix

y

numeric vector/matrix or big.matrix

ncomp

number of latent components

tol

numeric tolerance used in the core solver

backend

one of "auto", "arma", "bigmem"

mode

one of "auto", "pls1", "pls2"

algorithm

one of "auto", "simpls", "nipals", "kernelpls", "widekernelpls", "rkhs", "klogitpls", "sparse_kpls", "rkhs_xy", "kf_pls"

scores

one of "none", "r", "big"

chunk_size

chunk size for the bigmem backend

chunk_cols

columns chunk size for the bigmem backend

scores_name

name for dense scores (or output big.matrix)

scores_target

one of "auto", "new", "existing"

scores_bm

optional existing big.matrix or descriptor for scores

scores_backingfile

Character; file name for file-backed scores (when scores="big").

scores_backingpath

Character; directory for the file-backed scores. Defaults to getwd() or tempdir() in streamed predict, unless overridden.

scores_descriptorfile

Character; descriptor file name for the file-backed scores.

scores_colnames

optional character vector for score column names

return_scores_descriptor

logical; if TRUE and scores is big.matrix, add $scores_descriptor

coef_threshold

Optional non-negative value used to hard-threshold the fitted coefficients after model estimation. When supplied, absolute coefficients strictly below the threshold are set to zero via pls_threshold().

kernel

kernel name for RKHS/KPLS ("linear", "rbf", "poly", "sigmoid")

gamma

RBF/sigmoid/poly scale parameter

degree

polynomial degree

coef0

polynomial/sigmoid bias

approx

kernel approximation: "none", "nystrom", "rff"

approx_rank

rank (columns / features) for the approximation

class_weights

optional numeric weights for classes in klogitpls

Examples

set.seed(123)
X <- matrix(rnorm(60), nrow = 20)
y <- X[, 1] - 0.5 * X[, 2] + rnorm(20, sd = 0.1)
fit <- pls_fit(X, y, ncomp = 2, scores = "r", algorithm = "simpls")
head(pls_predict_response(fit, X, ncomp = 2))