enpls.od: Ensemble Partial Least Squares for Outlier Detection

In wind22zhu/enpls1.2: Ensemble Partial Least Squares Regression

Description

This function performs outlier detection with ensemble partial least squares.

Usage

enpls.od(x, y, maxcomp = NULL, MCtimes = 500L, method = c("mc",
  "bootstrap"), ratio = 0.8, parallel = 1L)

Arguments

x	predictor matrix
y	response vector
maxcomp	Maximum number of components included within the models, if not specified, default is the variable (column) numbers in x.
MCtimes	times of Monte-Carlo
method	"mc" or "bootstrap". Default is "mc".
ratio	sample ratio used when method = "mc"
parallel	Integer. Number of parallel processes to use. Default is 1, which means run serially.

Details

This function performs outlier detection with ensemble partial least squares.

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

Author(s)

Min-feng Zhu <[e-mail]>, Nan Xiao <[e-mail]>

References

DongSheng Cao, Yizeng Liang, Qingsong Xu, Hongdong Li, and Xian Chen. "A new strategy of outlier detection for QSAR/QSPR." Journal of computational chemistry 31, no. 3 (2010): 592–602.

Dongsheng Cao, Yizeng Liang, Qingsong Xu, Yifeng Yun, and Hongdong Li. "Toward better QSAR/QSPR modeling: simultaneous outlier detection and variable selection using distribution of model features." Journal of computer-aided molecular design 25, no. 1 (2011): 67–80.

See Also

See enpls.fs for feature selection with ensemble PLS. See enpls.en for ensemble PLS regression. See enpls.ad for applicability domain with ensemble PLS

Examples

data(alkanes)
x = alkanes$x
y = alkanes$y

set.seed(42)
od = enpls.od(x, y, MCtimes = 100)
print(od)
plot(od)
plot(od, criterion = 'sd')

wind22zhu/enpls1.2 documentation built on May 4, 2019, 6:31 a.m.