enspls.od: Ensemble Sparse Partial Least Squares for Outlier Detection

In enpls: Ensemble Partial Least Squares Regression

Ensemble Sparse Partial Least Squares for Outlier Detection

Description

Outlier detection with ensemble sparse partial least squares.

Usage

enspls.od(
  x,
  y,
  maxcomp = 5L,
  cvfolds = 5L,
  alpha = seq(0.2, 0.8, 0.2),
  reptimes = 500L,
  method = c("mc", "boot"),
  ratio = 0.8,
  parallel = 1L
)

Arguments

x	Predictor matrix.
y	Response vector.
maxcomp	Maximum number of components included within each model. If not specified, will use 5 by default.
cvfolds	Number of cross-validation folds used in each model for automatic parameter selection, default is 5.
alpha	Parameter (grid) controlling sparsity of the model. If not specified, default is seq(0.2, 0.8, 0.2).
reptimes	Number of models to build with Monte-Carlo resampling or bootstrapping.
method	Resampling method. "mc" (Monte-Carlo resampling) or "boot" (bootstrapping). Default is "mc".
ratio	Sampling ratio used when method = "mc".
parallel	Integer. Number of CPU cores to use. Default is 1 (not parallelized).

Value

A list containing four components:

• error.mean - error mean for all samples (absolute value)

• error.median - error median for all samples

• error.sd - error sd for all samples

• predict.error.matrix - the original prediction error matrix

Note

To maximize the probablity that each observation can be selected in the test set (thus the prediction uncertainty can be measured), please try setting a large reptimes.

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.fs for measuring feature importance with ensemble sparse partial least squares regressions. See enspls.fit for fitting ensemble sparse partial least squares regression models.

Examples

data("logd1k")
x <- logd1k$x
y <- logd1k$y

set.seed(42)
od <- enspls.od(
  x, y,
  reptimes = 5, maxcomp = 3,
  alpha = c(0.3, 0.6, 0.9)
)
plot(od, prob = 0.1)
plot(od, criterion = "sd", sdtimes = 1)

enpls documentation built on Aug. 8, 2025, 6:47 p.m.