enpls (version 5.6)

enpls.od: Ensemble Partial Least Squares for Outlier Detection

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

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')