Package 'DLEGFM'

Title: Distributed Loading Estimation for General Factor Model
Description: The load estimation method is based on a general factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.
Authors: Guangbao Guo [aut, cre, cph], Yaping Li [aut]
Maintainer: Guangbao Guo <[email protected]>
License: MIT + file LICENSE
Version: 0.4.0
Built: 2026-06-09 05:48:04 UTC
Source: https://github.com/cran/DLEGFM

Help Index

Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

BlPC(data,m)

Arguments

data

The data is total data set
m

The m is the number of first layer principal component

Value

ABr

estimation of load value
ABc

estimation of load value
DBr

estimation of error term
DBc

estimation of error term
SigmaB1hat

estimation of covariance
SigmaB2hat

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

BlPC(data=ISE,m=3)

Distributed Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

DBlPC(data,m,n1,K)

Arguments

data

The data is total data set
m

The m is the number of first layer principal component
n1

The n1 is the length of each data subset
K

The K is the number of nodes

Value

ABr

estimation of load value
ABc

estimation of load value
DBr

estimation of error term
DBc

estimation of error term
SigmaB1hat

estimation of covariance
SigmaB2hat

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

DBlPC(data=ISE,m=3,n1=107,K=5)

Distributed Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

DFanPC(data,m,n1,K)

Arguments

data

The data is total data set
m

The m is the number of principal component
n1

The n1 is the length of each data subset
K

The K is the number of nodes

Value

AF

estimation of load value
DF

estimation of error term
SigmahatF

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

DFanPC(data=ISE,m=3,n1=107,K=5)

Distributed Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

DGaoPC(data,m,n1,K)

Arguments

data

The data is total data set
m

The m is the number of first layer principal component
n1

The n1 is the length of each data subset
K

The K is the number of nodes

Value

AG1

estimation of load value
AG2

estimation of load value
DG1

estimation of error term
DG2

estimation of error term
SigmahatG1

estimation of covariance
SigmahatG2

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

DGaoPC(data=ISE,m=3,n1=107,K=5)

Distributed Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

DGulPC(data,m,n1,K)

Arguments

data

The data is total data set
m

The m is the number of first layer principal component
n1

The n1 is the length of each data subset
K

The K is the number of nodes

Value

AU1

estimation of load value
AU2

estimation of load value
DU3

estimation of error term
S1hat

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

DGulPC(data=ISE,m=3,n1=107,K=5)

Dow Jones industrial average

Description

The Dow Jones industrial average (DJIA) data set.

Usage

data("DJIA")

Format

GAS.F

a numeric vector

Nikkei.F

a numeric vector

NZD

a numeric vector

silver.F

a numeric vector

RUSSELL.F

a numeric vector

S.P.F

a numeric vector

CHF

a numeric vector

Dollar.index.F

a numeric vector

Dollar.index

a numeric vector

wheat.F

a numeric vector

XAG

a numeric vector

XAU

a numeric vector

Details

The data set comes from the Dow Jones industrial average (PSA) data of 96 patients collected by Stanford University Medical Center. These patients all underwent radical prostatectomy.

Source

The Stanford University Medical Center.

References

NA

Examples

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

Distributed Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

DPC(data,m,n1,K)

Arguments

data

The data is total data set
m

The m is the number of first layer principal component
n1

The n1 is the length of each data subset
K

The K is the number of nodes

Value

Ahat

estimation of load value
Dhat

estimation of error term
Sigmahat

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

DPC(data=ISE,m=3,n1=107,K=5)

Distributed Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

DPPC(data,m,n1,K)

Arguments

data

The data is total data set
m

The m is the number of first layer principal component
n1

The n1 is the length of each data subset
K

The K is the number of nodes

Value

Apro

estimation of load value
Dpro

estimation of error term
Sigmahatpro

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

DPPC(data=ISE,m=3,n1=107,K=5)

Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

FanPC(data,m)

Arguments

data

The data is total data set
m

The m is the number of principal component

Value

AF

estimation of load value
DF

estimation of error term
SigmahatF

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

FanPC(data=ISE,m=3)

Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

GaoPC(data,m)

Arguments

data

The data is total data set
m

The m is the number of principal component

Value

AG1

estimation of load value
AG2

estimation of load value
DG1

estimation of error term
DG2

estimation of error term
SigmahatG1

estimation of covariance
SigmahatG2

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

GaoPC(data=ISE,m=3)

Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

GulPC(data,m)

Arguments

data

The data is total data set
m

The m is the number of first layer principal component

Value

AU1

estimation of load value
AU2

estimation of load value
DU3

estimation of error term
Shat

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

GulPC(data=ISE,m=3)

Istanbul Stock Exchange

Description

The Istanbul Stock Exchange (ISE) data set.

Usage

data("ISE")

Format

ISE

a numeric vector

SP

a numeric vector

DAX

a numeric vector

FTSE

a numeric vector

NIKKEI

a numeric vector

BOVESPA

a numeric vector

EU

a numeric vector

EM

a numeric vector

Details

The data set comes from the Istanbul Stock Exchange (ISE) data of 96 patients collected by Stanford University Medical Center. These patients all underwent radical prostatectomy.

Source

The Stanford University Medical Center.

References

NA

Examples

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

Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

PC(data,m)

Arguments

data

The data is a highly correlated data set
m

The m is the number of principal component

Value

Ahat

estimation of load value
Dhat

estimation of error term
Sigmahat

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

PC(data=ISE,m=3)

Loading Estimation for General Factor Model

Description

This function estimates the load and residual terms based on the general factor model and calculates the estimated values.

Usage

PPC(data,m)

Arguments

data

The data is total data set
m

The m is the number of principal component

Value

Apro

estimation of load value
Dpro

estimation of error term
Sigmahatpro

estimation of covariance

Author(s)

Guangbao Guo, Yaping Li

Examples

PPC(data=ISE,m=3)

New York Stock Exchange Composite Index

Description

The New York Stock Exchange Composite Index SECI(SECI) data set.

Usage

data("SECI")

Format

GBP

a numeric vector

JPY

a numeric vector

CAD

a numeric vector

AAPL

a numeric vector

AMZN

a numeric vector

GE

a numeric vector

JPM

a numeric vector

MSFT

a numeric vector

WFC

a numeric vector

XOM

a numeric vector

FCHI

a numeric vector

FTSE

a numeric vector

GDAXI

a numeric vector

Details

The data set comes from the prostate specific antigen (PSA) data of 96 patients collected by Stanford University Medical Center. These patients all underwent radical prostatectomy.

Source

The Stanford University Medical Center.

References

NA

Examples

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

Stock Portfolio Performance

Description

The Stock Portfolio Performance (SPP) data set.

Usage

data("SPP")

Format

X1

a numeric vector

X2

a numeric vector

X3

a numeric vector

X4

a numeric vector

X5

a numeric vector

X6

a numeric vector

X7

a numeric vector

X8

a numeric vector

X9

a numeric vector

X10

a numeric vector

Details

The data set comes from the Stock Portfolio Performance (SPP) data of 96 patients collected by Stanford University Medical Center. These patients all underwent radical prostatectomy.

Source

The Stanford University Medical Center.

References

NA

Examples

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