enpls.od: Ensemble Partial Least Squares for Outlier Detection

In road2stat/enpls: Ensemble Partial Least Squares Regression

Description

Outlier detection with ensemble partial least squares.

Usage

enpls.od(
  x,
  y,
  maxcomp = NULL,
  cvfolds = 5L,
  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 the maximum number possible (considering cross-validation and special cases where n is smaller than p).
cvfolds	Number of cross-validation folds used in each model for automatic parameter selection, default is 5.
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 enpls.fs for measuring feature importance with ensemble partial least squares regressions. See enpls.fit for fitting ensemble partial least squares regression models.

Examples

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

set.seed(42)
od <- enpls.od(x, y, reptimes = 50)
print(od)
plot(od)
plot(od, criterion = "sd")

road2stat/enpls documentation built on Dec. 30, 2021, 2:20 a.m.