Package 'DLPCA'

Title: The Distributed Local PCA Algorithm
Description: Algorithm to handle with optimal subset selection for distributed local principal component analysis. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.
Authors: Guangbao Guo [aut, cre] , Guoqi Qian [aut], Yixiao Liu [aut], Haoyue Song [aut]
Maintainer: Guangbao Guo <[email protected]>
License: MIT + file LICENSE
Version: 0.0.5
Built: 2026-05-19 10:09:42 UTC
Source: https://github.com/cran/DLPCA

Help Index

Application

Description

Application data set

Usage

data("Application")

Format

The format is: int [1:48, 1:15] 6 9 7 5 6 7 9 9 9 4 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:15] "FL" "APP" "AA" "LA" ...

Details

It is the scoring of 15 indicators on 48 interviewees

Examples

data(Application)
## maybe str(Application) ; plot(Application) ...

Distributed local PCA

Description

Calculate the estimator on the DLPCA method

Usage

DLPCA(X = X, n = n, p = p, m = m, K = K, L = L)

Arguments

X

is the original data matrix
n

is the sample size
p

is the number of variables
m

is the number of eigenvalues
K

is the number of nodes
L

is the number of subgroups

Value

time

is the time cost
V

is the right singular matrix
Vm

is the m-right singular matrix
Smean

is the mean covariance matrix
MMSER

is the mean MSE values of the robust covariance matrix sub-estimators
MMSES

is the mean MSE values of the covariance matrix sub-estimators
MMSEX

is the mean MSE values of the sub-estimators of the matrix X

MSER

is the min MSE values of the robust covariance matrix sub-estimators
MSES

is the min MSE values of the covariance matrix sub-estimators
MSEX

is the min MSE values of the sub-estimators of the matrix X
wMSER

is the location of the min MSE values of the robust covariance matrix sub-estimators
wMSES

is the location of the min MSE values of the covariance matrix sub-estimators
wMSEX

is the location of the min MSE values of the sub-estimators of the matrix X
sigm

is the estimator of the covariance matrix of the matrix X

Examples

data(Application)
X=Application
n=nrow(Application);p=ncol(Application)
m=5;L=4;K=4
DLPCA_result=DLPCA(X=X,n=n,p=p,m=m,K=K,L=L)

Gas-Turbine CO and NOx Emission Data

Description

Gas-Turbine CO and NOx Emission Data in 2011

Usage

data("gt2011")

Format

A data frame with 7411 observations on the following 11 variables.

AT

a numeric vector

AP

a numeric vector

AH

a numeric vector

AFDP

a numeric vector

GTEP

a numeric vector

TIT

a numeric vector

TAT

a numeric vector

TEY

a numeric vector

CDP

a numeric vector

CO

a numeric vector

NOX

a numeric vector

Details

The dataset contains 36733 instances of 11 sensor measures aggregated over one hour, from a gas turbine located in Turkey for the purpose of studying flue gas emissions, namely CO and NOx.

Source

Heysem Kaya, Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, The Netherlands

Examples

data(gt2011)

Gas-Turbine CO and NOx Emission Data

Description

Gas-Turbine CO and NOx Emission Data in 2012

Usage

data("gt2012")

Format

A data frame with 7628 observations on the following 11 variables.

AT

a numeric vector

AP

a numeric vector

AH

a numeric vector

AFDP

a numeric vector

GTEP

a numeric vector

TIT

a numeric vector

TAT

a numeric vector

TEY

a numeric vector

CDP

a numeric vector

CO

a numeric vector

NOX

a numeric vector

Details

The dataset contains 36733 instances of 11 sensor measures aggregated over one hour, from a gas turbine located in Turkey for the purpose of studying flue gas emissions, namely CO and NOx.

Source

Heysem Kaya, Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, The Netherlands

Examples

data(gt2012)

Gas-Turbine CO and NOx Emission Data

Description

Gas-Turbine CO and NOx Emission Data in 2013

Usage

data("gt2013")

Format

A data frame with 7152 observations on the following 11 variables.

AT

a numeric vector

AP

a numeric vector

AH

a numeric vector

AFDP

a numeric vector

GTEP

a numeric vector

TIT

a numeric vector

TAT

a numeric vector

TEY

a numeric vector

CDP

a numeric vector

CO

a numeric vector

NOX

a numeric vector

Details

The dataset contains 36733 instances of 11 sensor measures aggregated over one hour, from a gas turbine located in Turkey for the purpose of studying flue gas emissions, namely CO and NOx.

Source

Heysem Kaya, Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, The Netherlands

Examples

data(gt2013)

Gas-Turbine CO and NOx Emission Data

Description

Gas-Turbine CO and NOx Emission Data in 2014

Usage

data("gt2014")

Format

A data frame with 7158 observations on the following 11 variables.

AT

a numeric vector

AP

a numeric vector

AH

a numeric vector

AFDP

a numeric vector

GTEP

a numeric vector

TIT

a numeric vector

TAT

a numeric vector

TEY

a numeric vector

CDP

a numeric vector

CO

a numeric vector

NOX

a numeric vector

Details

The dataset contains 36733 instances of 11 sensor measures aggregated over one hour, from a gas turbine located in Turkey for the purpose of studying flue gas emissions, namely CO and NOx.

Source

Heysem Kaya, Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, The Netherlands

Examples

data(gt2014)

Gas-Turbine CO and NOx Emission Data

Description

Gas-Turbine CO and NOx Emission Data in 2015

Usage

data("gt2015")

Format

A data frame with 7384 observations on the following 11 variables.

AT

a numeric vector

AP

a numeric vector

AH

a numeric vector

AFDP

a numeric vector

GTEP

a numeric vector

TIT

a numeric vector

TAT

a numeric vector

TEY

a numeric vector

CDP

a numeric vector

CO

a numeric vector

NOX

a numeric vector

Details

The dataset contains 36733 instances of 11 sensor measures aggregated over one hour, from a gas turbine located in Turkey for the purpose of studying flue gas emissions, namely CO and NOx.

Source

Heysem Kaya, Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, The Netherlands

Examples

data(gt2015)

Iris

Description

Iris data set

Usage

data("Iris")

Format

A data frame with 150 observations on the following 5 variables.

Sepal.length

a numeric vector

Sepal.width

a numeric vector

Petal.length

a numeric vector

Petal.width

a numeric vector

Species

a character vector

Details

It contains 150 samples with 5 variables

Source

Gaspar peninsula in Canada

Examples

data(Iris)
## maybe str(Iris) ; plot(Iris) ...

MSE on PCA

Description

Caculate the MSE value on PCA

Usage

MSEpca(V = V, X = X, n = n, p = p, m = m, K = K, L = L)

Arguments

V

is the right singular matrix
X

is the orignal data set
n

is the sample size
p

is the number of variables
m

is the number of eigenvalues
K

is the number of nodes
L

is the number of subgroups

Value

MSEpca

the MSE value on PCA

Examples

data(Application)
X=Application
n=nrow(Application);p=ncol(Application)
m=5;L=4;K=4
DLPCA_result=DLPCA(X=X,n=n,p=p,m=m,K=K,L=L)                  
V=DLPCA_result$V
MSEpca_result=MSEpca(V=V,X=X,n=n,p=p,m=m,K=K,L=L) 
MSE_PCA=MSEpca_result$MSEpca