Type:	Package
Title:	Ensemble Partial Least Squares Regression
Version:	6.1
Maintainer:	Nan Xiao <me@nanx.me>
Description:	An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.
License:	GPL-3 | file LICENSE
URL:	https://nanx.me/enpls/, https://github.com/nanxstats/enpls
BugReports:	https://github.com/nanxstats/enpls/issues
Depends:	R (≥ 3.0.2)
LazyData:	TRUE
VignetteBuilder:	knitr
Imports:	pls, spls, foreach, doParallel, ggplot2, reshape2, plotly
Suggests:	knitr, rmarkdown
Encoding:	UTF-8
RoxygenNote:	6.1.1
NeedsCompilation:	no
Packaged:	2019-05-18 00:13:39 UTC; nanx
Author:	Nan Xiao [aut, cre], Dong-Sheng Cao [aut], Miao-Zhu Li [aut], Qing-Song Xu [aut]
Repository:	CRAN
Date/Publication:	2019-05-18 04:30:02 UTC

enpls: Ensemble Partial Least Squares Regression

Description

An algorithmic framework for measuring feature importance, outlier detection, model applicability domain evaluation, and ensemble predictive modeling with (sparse) partial least squares regressions.

Author(s)

Maintainer: Nan Xiao me@nanx.me (0000-0002-0250-5673)

Authors:

• Dong-Sheng Cao

• Miao-Zhu Li miaozhu.li@duke.edu

• Qing-Song Xu qsxu@csu.edu.cn

See Also

Useful links:

• https://nanx.me/enpls/

• https://github.com/nanxstats/enpls

• Report bugs at https://github.com/nanxstats/enpls/issues

Methylalkanes Retention Index Dataset

Description

Methylalkanes retention index dataset from Liang et, al.

Usage

data("alkanes")

Format

A list with 2 components:

• x - data frame with 207 rows (samples) and 21 columns (predictors)

• y - numeric vector of length 207 (response)

Details

This dataset contains 207 methylalkanes' chromatographic retention index (y) which have been modeled by 21 molecular descriptors (x).

Molecular descriptor types:

• Chi path, cluster and path/cluster indices

• Kappa shape indices

• E-state indices

• Molecular electricity distance vector index

References

Yi-Zeng Liang, Da-Lin Yuan, Qing-Song Xu, and Olav Martin Kvalheim. "Modeling based on subspace orthogonal projections for QSAR and QSPR research." Journal of Chemometrics 22, no. 1 (2008): 23–35.

Examples

data("alkanes")
str(alkanes)

Cross Validation for Ensemble Partial Least Squares Regression

Description

K-fold cross validation for ensemble partial least squares regression.

Usage

cv.enpls(x, y, nfolds = 5L, verbose = TRUE, ...)

Arguments

Value

A list containing:

• ypred - a matrix containing two columns: real y and predicted y

• residual - cross validation result (y.pred - y.real)

• RMSE - RMSE

• MAE - MAE

• Rsquare - Rsquare

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.fit for ensemble partial least squares regressions.

Examples

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

set.seed(42)
cvfit <- cv.enpls(x, y, reptimes = 10)
print(cvfit)
plot(cvfit)

Cross Validation for Ensemble Sparse Partial Least Squares Regression

Description

K-fold cross validation for ensemble sparse partial least squares regression.

Usage

cv.enspls(x, y, nfolds = 5L, verbose = TRUE, ...)

Arguments

Value

A list containing:

• ypred - a matrix containing two columns: real y and predicted y

• residual - cross validation result (y.pred - y.real)

• RMSE - RMSE

• MAE - MAE

• Rsquare - Rsquare

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.fit for ensemble sparse partial least squares regressions.

Examples

# This example takes one minute to run
## Not run: 
data("logd1k")
x <- logd1k$x
y <- logd1k$y

set.seed(42)
cvfit <- cv.enspls(x, y, reptimes = 10)
print(cvfit)
plot(cvfit)

## End(Not run)

Ensemble Partial Least Squares for Model Applicability Domain Evaluation

Description

Model applicability domain evaluation with ensemble partial least squares.

Usage

enpls.ad(x, y, xtest, ytest, maxcomp = NULL, cvfolds = 5L,
  space = c("sample", "variable"), method = c("mc", "boot"),
  reptimes = 500L, ratio = 0.8, parallel = 1L)

Arguments

Value

A list containing:

• tr.error.mean - absolute mean prediction error for training set

• tr.error.median - absolute median prediction error for training set

• tr.error.sd - prediction error sd for training set

• tr.error.matrix - raw prediction error matrix for training set

• te.error.mean - list of absolute mean prediction error for test set(s)

• te.error.median - list of absolute median prediction error for test set(s)

• te.error.sd - list of prediction error sd for test set(s)

• te.error.matrix - list of raw prediction error matrix for test set(s)

Note

Note that for space = "variable", method could only be "mc", since bootstrapping in the variable space will create duplicated variables, and that could cause problems.

Author(s)

Nan Xiao <https://nanx.me>

Examples

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

# training set
x.tr <- x[1:100, ]
y.tr <- y[1:100]

# two test sets
x.te <- list(
  "test.1" = x[101:150, ],
  "test.2" = x[151:207, ]
)
y.te <- list(
  "test.1" = y[101:150],
  "test.2" = y[151:207]
)

set.seed(42)
ad <- enpls.ad(
  x.tr, y.tr, x.te, y.te,
  space = "variable", method = "mc",
  ratio = 0.9, reptimes = 50
)
print(ad)
plot(ad)
# the interactive plot requires a HTML viewer
## Not run: 
plot(ad, type = "interactive")

## End(Not run)

core fitting function for enpls.ad

Description

select the best ncomp with cross-validation and use it to fit the complete training set. scale = TRUE

Usage

enpls.ad.core.fit(trainingset, maxcomp, cvfolds)

Value

the fitted plsr object

core prediction function for enpls.ad

Description

core prediction function for enpls.ad

Usage

enpls.ad.core.pred(model, testset)

Value

the error vector between predicted and real response

Ensemble Partial Least Squares Regression

Description

Ensemble partial least squares regression.

Usage

enpls.fit(x, y, maxcomp = NULL, cvfolds = 5L, reptimes = 500L,
  method = c("mc", "boot"), ratio = 0.8, parallel = 1L)

Arguments

Value

A list containing all partial least squares model objects.

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.fs for measuring feature importance with ensemble partial least squares regressions. See enpls.od for outlier detection with ensemble partial least squares regressions.

Examples

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

set.seed(42)
fit <- enpls.fit(x, y, reptimes = 50)
print(fit)
predict(fit, newx = x)

core function for enpls.fit

Description

select the best ncomp with cross-validation and use it to fit the complete training set. scale = TRUE

Usage

enpls.fit.core(plsdf, maxcomp, cvfolds)

Value

the coefficients

Ensemble Partial Least Squares for Measuring Feature Importance

Description

Measuring feature importance with ensemble partial least squares.

Usage

enpls.fs(x, y, maxcomp = NULL, cvfolds = 5L, reptimes = 500L,
  method = c("mc", "boot"), ratio = 0.8, parallel = 1L)

Arguments

Value

A list containing two components:

• variable.importance - a vector of variable importance

• coefficient.matrix - original coefficient matrix

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.od for outlier detection 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)
fs <- enpls.fs(x, y, reptimes = 50)
print(fs)
plot(fs)

core function for enpls.fs

Description

select the best ncomp with cross-validation and use it to fit the complete training set. scale = FALSE

Usage

enpls.fs.core(plsdf, maxcomp, cvfolds)

Value

fitted coefficients

Mean Absolute Error (MAE)

Description

Mean Absolute Error (MAE)

Usage

enpls.mae(yreal, ypred)

Arguments

Value

MAE

Author(s)

Nan Xiao <https://nanx.me>

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

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")

core function for enpls.od

Description

select the best ncomp with cross-validation and use it to fit the complete training set, then predict on the test set. scale = TRUE

Usage

enpls.od.core(plsdf.sample, plsdf.remain, maxcomp, cvfolds)

Value

the error vector between predicted y and real y

Root Mean Squared Error (RMSE)

Description

Compute Root Mean Squared Error (RMSE).

Usage

enpls.rmse(yreal, ypred)

Arguments

Value

RMSE

Author(s)

Nan Xiao <https://nanx.me>

Root Mean Squared Logarithmic Error (RMSLE)

Description

Root Mean Squared Logarithmic Error (RMSLE)

Usage

enpls.rmsle(yreal, ypred)

Arguments

Value

RMSLE

Author(s)

Nan Xiao <https://nanx.me>

Ensemble Sparse Partial Least Squares for Model Applicability Domain Evaluation

Description

Model applicability domain evaluation with ensemble sparse partial least squares.

Usage

enspls.ad(x, y, xtest, ytest, maxcomp = 5L, cvfolds = 5L,
  alpha = seq(0.2, 0.8, 0.2), space = c("sample", "variable"),
  method = c("mc", "boot"), reptimes = 500L, ratio = 0.8,
  parallel = 1L)

Arguments

Value

A list containing:

• tr.error.mean - absolute mean prediction error for training set

• tr.error.median - absolute median prediction error for training set

• tr.error.sd - prediction error sd for training set

• tr.error.matrix - raw prediction error matrix for training set

• te.error.mean - list of absolute mean prediction error for test set(s)

• te.error.median - list of absolute median prediction error for test set(s)

• te.error.sd - list of prediction error sd for test set(s)

• te.error.matrix - list of raw prediction error matrix for test set(s)

Note

Note that for space = "variable", method could only be "mc", since bootstrapping in the variable space will create duplicated variables, and that could cause problems.

Author(s)

Nan Xiao <https://nanx.me>

Examples

data("logd1k")
# remove low variance variables
x <- logd1k$x[, -c(17, 52, 59)]
y <- logd1k$y

# training set
x.tr <- x[1:300, ]
y.tr <- y[1:300]

# two test sets
x.te <- list(
  "test.1" = x[301:400, ],
  "test.2" = x[401:500, ]
)
y.te <- list(
  "test.1" = y[301:400],
  "test.2" = y[401:500]
)

set.seed(42)
ad <- enspls.ad(
  x.tr, y.tr, x.te, y.te,
  maxcomp = 3, alpha = c(0.3, 0.6, 0.9),
  space = "variable", method = "mc",
  ratio = 0.8, reptimes = 10
)
print(ad)
plot(ad)
# the interactive plot requires a HTML viewer
## Not run: 
plot(ad, type = "interactive")

## End(Not run)

core fitting function for enspls.ad

Description

select the best ncomp and alpha with cross-validation, then use them to fit the complete training set. scale = TRUE

Usage

enspls.ad.core.fit(x.tr, y.tr, maxcomp, cvfolds, alpha)

Value

the fitted spls object

core prediction function for enspls.ad

Description

core prediction function for enspls.ad

Usage

enspls.ad.core.pred(model, x.te, y.te)

Value

the error vector between predicted and real response

Ensemble Sparse Partial Least Squares Regression

Description

Ensemble sparse partial least squares regression.

Usage

enspls.fit(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

Value

A list containing all sparse partial least squares model objects.

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.od for outlier detection with ensemble sparse partial least squares regressions.

Examples

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

set.seed(42)
fit <- enspls.fit(
  x, y,
  reptimes = 5, maxcomp = 3,
  alpha = c(0.3, 0.6, 0.9)
)
print(fit)
predict(fit, newx = x)

core function for enspls.fit

Description

select the best ncomp and alpha, then use them to fit the complete training set. scale = TRUE

Usage

enspls.fit.core(xtmp, ytmp, maxcomp, cvfolds, alpha)

Value

the coefficients

Ensemble Sparse Partial Least Squares for Measuring Feature Importance

Description

Measuring feature importance with ensemble sparse partial least squares.

Usage

enspls.fs(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

Value

A list containing two components:

• variable.importance - a vector of variable importance

• coefficient.matrix - original coefficient matrix

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.od for outlier detection 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)
fs <- enspls.fs(x, y, reptimes = 5, maxcomp = 2)
print(fs, nvar = 10)
plot(fs, nvar = 10)
plot(fs, type = "boxplot", limits = c(0.05, 0.95), nvar = 10)

core function for enspls.fs

Description

select the best ncomp and alpha with cross-validation, then use it to fit the complete training set. scale = FALSE

Usage

enspls.fs.core(xtmp, ytmp, maxcomp, cvfolds, alpha)

Value

fitted coefficients

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

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)

core function for enspls.od

Description

select the best ncomp and alpha with cross-validation, then use them to fit the complete training set, and predict on the test set. scale = TRUE

Usage

enspls.od.core(x.sample, y.sample, x.remain, y.remain, maxcomp, cvfolds,
  alpha)

Value

the error vector between predicted y and real y

logD7.4 Data for 1,000 Compounds

Description

Distribution coefficients at pH 7.4 (logD7.4) dataset from Wang et, al.

Usage

data(logd1k)

Format

A list with 2 components:

• x - data frame with 1,000 rows (samples) and 80 columns (predictors)

• y - numeric vector of length 1,000 (response)

The first 1000 compounds in the original dataset were selected.

Details

This dataset contains distribution coefficients at pH 7.4 (logD7.4) for 1,000 compounds, and 80 molecular descriptors computed with RDKit.

References

Jian-Bing Wang, Dong-Sheng Cao, Min-Feng Zhu, Yong-Huan Yun, Nan Xiao, and Yi-Zeng Liang. "In silico evaluation of logD7.4 and comparison with other prediction methods." Journal of Chemometrics 29, no. 7 (2015): 389–398.

Examples

data(logd1k)
str(logd1k)

Plot cv.enpls object

Description

Plot cv.enpls object

Usage

## S3 method for class 'cv.enpls'
plot(x, xlim = NULL, ylim = NULL, alpha = 0.8,
  main = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See cv.enpls for cross-validation of ensemble partial least squares regression models.

Examples

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

set.seed(42)
cvfit <- cv.enpls(x, y, reptimes = 10)
plot(cvfit)

Plot cv.enspls object

Description

Plot cv.enspls object

Usage

## S3 method for class 'cv.enspls'
plot(x, xlim = NULL, ylim = NULL, alpha = 0.8,
  main = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See cv.enspls for cross-validation of ensemble sparse partial least squares regression models.

Examples

# This example takes one minute to run
## Not run: 
data("logd1k")
x <- logd1k$x
y <- logd1k$y

set.seed(42)
cvfit <- cv.enspls(x, y, reptimes = 10)
plot(cvfit)

## End(Not run)

Plot enpls.ad object

Description

Plot enpls.ad object

Usage

## S3 method for class 'enpls.ad'
plot(x, type = c("static", "interactive"),
  main = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.ad for model applicability domain evaluation with ensemble partial least squares regressions.

Examples

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

# training set
x.tr <- x[1:100, ]
y.tr <- y[1:100]

# two test sets
x.te <- list(
  "test.1" = x[101:150, ],
  "test.2" = x[151:207, ]
)
y.te <- list(
  "test.1" = y[101:150],
  "test.2" = y[151:207]
)

set.seed(42)
ad <- enpls.ad(
  x.tr, y.tr, x.te, y.te,
  space = "variable", method = "mc",
  ratio = 0.9, reptimes = 50
)
plot(ad)
# the interactive plot requires a HTML viewer
## Not run: 
plot(ad, type = "interactive")

## End(Not run)

Plot enpls.fs object

Description

Plot enpls.fs object

Usage

## S3 method for class 'enpls.fs'
plot(x, nvar = NULL, type = c("dotplot", "boxplot"),
  limits = c(0, 1), main = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.fs for measuring feature importance with ensemble partial least squares regressions.

Examples

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

set.seed(42)
fs <- enpls.fs(x, y, reptimes = 50)
plot(fs)
plot(fs, nvar = 10)
plot(fs, type = "boxplot")
plot(fs, type = "boxplot", limits = c(0.05, 0.95))

Plot enpls.od object

Description

Plot enpls.od object

Usage

## S3 method for class 'enpls.od'
plot(x, criterion = c("quantile", "sd"),
  prob = 0.05, sdtimes = 3L, alpha = 1, main = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.od for outlier detection with ensemble partial least squares regressions.

Examples

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

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

Plot enspls.ad object

Description

Plot enspls.ad object

Usage

## S3 method for class 'enspls.ad'
plot(x, type = c("static", "interactive"),
  main = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.ad for model applicability domain evaluation with ensemble sparse partial least squares regressions.

Examples

data("logd1k")
# remove low variance variables
x <- logd1k$x[, -c(17, 52, 59)]
y <- logd1k$y

# training set
x.tr <- x[1:300, ]
y.tr <- y[1:300]

# two test sets
x.te <- list(
  "test.1" = x[301:400, ],
  "test.2" = x[401:500, ]
)
y.te <- list(
  "test.1" = y[301:400],
  "test.2" = y[401:500]
)

set.seed(42)
ad <- enspls.ad(
  x.tr, y.tr, x.te, y.te,
  maxcomp = 3, alpha = c(0.3, 0.6, 0.9),
  space = "variable", method = "mc",
  ratio = 0.8, reptimes = 10
)
plot(ad)
# the interactive plot requires a HTML viewer
## Not run: 
plot(ad, type = "interactive")

## End(Not run)

Plot enspls.fs object

Description

Plot enspls.fs object

Usage

## S3 method for class 'enspls.fs'
plot(x, nvar = NULL, type = c("dotplot",
  "boxplot"), limits = c(0, 1), main = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.fs for measuring feature importance with ensemble sparse partial least squares regressions.

Examples

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

set.seed(42)
fs <- enspls.fs(x, y, reptimes = 5, maxcomp = 2)
plot(fs, nvar = 10)
plot(fs, type = "boxplot", limits = c(0.05, 0.95), nvar = 10)

Plot enspls.od object

Description

Plot enspls.od object

Usage

## S3 method for class 'enspls.od'
plot(x, criterion = c("quantile", "sd"),
  prob = 0.05, sdtimes = 3L, alpha = 1, main = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.od for outlier detection with ensemble sparse partial least squares regressions.

Examples

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

set.seed(42)
od <- enspls.od(x, y, reptimes = 4, maxcomp = 2)
plot(od, criterion = "quantile", prob = 0.1)
plot(od, criterion = "sd", sdtimes = 1)

Make Predictions from a Fitted Ensemble Partial Least Squares Model

Description

Make predictions on new data by fitted enpls.fit object.

Usage

## S3 method for class 'enpls.fit'
predict(object, newx, method = c("mean", "median"),
  ...)

Arguments

Value

A numeric vector containing the predicted values.

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.fit for fitting ensemble partial least squares regression models.

Examples

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

set.seed(42)
fit <- enpls.fit(x, y, reptimes = 50)
y.pred <- predict(fit, newx = x)
plot(y, y.pred, xlim = range(y), ylim = range(y))
abline(a = 0L, b = 1L)
y.pred.med <- predict(fit, newx = x, method = "median")
plot(y, y.pred.med, xlim = range(y), ylim = range(y))
abline(a = 0L, b = 1L)

Make Predictions from a Fitted Sparse Ensemble Partial Least Squares Model

Description

Make predictions on new data by fitted enspls.fit object.

Usage

## S3 method for class 'enspls.fit'
predict(object, newx, method = c("mean", "median"),
  ...)

Arguments

Value

A numeric vector containing the predicted values.

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.fit for fitting ensemble sparse partial least squares regression models.

Examples

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

set.seed(42)
fit <- enspls.fit(x, y, reptimes = 5, maxcomp = 2)
y.pred <- predict(fit, newx = x)
plot(y, y.pred, xlim = range(y), ylim = range(y))
abline(a = 0L, b = 1L)
y.pred.med <- predict(fit, newx = x, method = "median")
plot(y, y.pred.med, xlim = range(y), ylim = range(y))
abline(a = 0L, b = 1L)

Print cv.enpls Object

Description

Print cv.enpls object.

Usage

## S3 method for class 'cv.enpls'
print(x, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See cv.enpls for cross-validation of ensemble partial least squares regression models.

Examples

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

set.seed(42)
cvfit <- cv.enpls(x, y, reptimes = 10)
cvfit

Print cv.enspls Object

Description

Print cv.enspls object.

Usage

## S3 method for class 'cv.enspls'
print(x, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See cv.enspls for cross-validation of ensemble sparse partial least squares regression models.

Examples

# This example takes one minute to run
## Not run: 
data("logd1k")
x <- logd1k$x
y <- logd1k$y

set.seed(42)
cvfit <- cv.enspls(x, y, reptimes = 10)
print(cvfit)

## End(Not run)

Print enpls.ad Object

Description

Print enpls.ad object.

Usage

## S3 method for class 'enpls.ad'
print(x, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.ad for model applicability domain evaluation with ensemble partial least squares regressions.

Examples

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

# training set
x.tr <- x[1:100, ]
y.tr <- y[1:100]

# two test sets
x.te <- list(
  "test.1" = x[101:150, ],
  "test.2" = x[151:207, ]
)
y.te <- list(
  "test.1" = y[101:150],
  "test.2" = y[151:207]
)

set.seed(42)
ad <- enpls.ad(
  x.tr, y.tr, x.te, y.te,
  space = "variable", method = "mc",
  ratio = 0.9, reptimes = 50
)
ad

Print Fitted Ensemble Partial Least Squares Object

Description

Print coefficients of each model in the enpls.fit object.

Usage

## S3 method for class 'enpls.fit'
print(x, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.fit for fitting ensemble partial least squares regression models.

Examples

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

set.seed(42)
fit <- enpls.fit(x, y, reptimes = 50)
fit

Print enpls.fs Object

Description

Print enpls.fs object.

Usage

## S3 method for class 'enpls.fs'
print(x, sort = TRUE, nvar = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.fs for measuring feature importance with ensemble partial least squares regressions.

Examples

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

set.seed(42)
fs <- enpls.fs(x, y, reptimes = 100)
print(fs)
print(fs, nvar = 10L)

Print enpls.od Object

Description

Print enpls.od object.

Usage

## S3 method for class 'enpls.od'
print(x, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enpls.od for outlier detection with ensemble partial least squares regressions.

Examples

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

set.seed(42)
od <- enpls.od(x, y, reptimes = 40)
od

Print enspls.ad Object

Description

Print enspls.ad object.

Usage

## S3 method for class 'enspls.ad'
print(x, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.ad for model applicability domain evaluation with ensemble sparse partial least squares regressions.

Examples

data("logd1k")
# remove low variance variables
x <- logd1k$x[, -c(17, 52, 59)]
y <- logd1k$y

# training set
x.tr <- x[1:300, ]
y.tr <- y[1:300]

# two test sets
x.te <- list(
  "test.1" = x[301:400, ],
  "test.2" = x[401:500, ]
)
y.te <- list(
  "test.1" = y[301:400],
  "test.2" = y[401:500]
)

set.seed(42)
ad <- enspls.ad(
  x.tr, y.tr, x.te, y.te,
  maxcomp = 3, alpha = c(0.3, 0.6, 0.9),
  space = "variable", method = "mc",
  ratio = 0.8, reptimes = 10
)
print(ad)

Print Fitted Ensemble Sparse Partial Least Squares Object

Description

Print coefficients of each model in the enspls.fit object.

Usage

## S3 method for class 'enspls.fit'
print(x, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.fit for fitting ensemble sparse partial least squares regression models.

Examples

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

set.seed(42)
fit <- enspls.fit(
  x, y,
  reptimes = 5, maxcomp = 3,
  alpha = c(0.3, 0.6, 0.9)
)
print(fit)

Print enspls.fs Object

Description

Print enspls.fs object.

Usage

## S3 method for class 'enspls.fs'
print(x, sort = TRUE, nvar = NULL, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.fs for measuring feature importance with ensemble sparse partial least squares regressions.

Examples

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

set.seed(42)
fs <- enspls.fs(
  x, y,
  reptimes = 5, maxcomp = 3,
  alpha = c(0.3, 0.6, 0.9)
)
print(fs, nvar = 10L)

Print enspls.od Object

Description

Print enspls.od object.

Usage

## S3 method for class 'enspls.od'
print(x, ...)

Arguments

Author(s)

Nan Xiao <https://nanx.me>

See Also

See enspls.od for outlier detection with ensemble sparse partial least squares regressions.

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)
)
print(od)

Add transparency level to hex colors

Description

Add transparency level to hex colors

Usage

rgb2alpha(raw_col, alpha)

Value

Color hex value (with transparency)