Title: | The Online Principal Component Estimation Method |
Date: | 2023-05-15 |
Version: | 0.0.2 |
Description: | The online principal component method can process the online data set. The philosophy of the package is described in Guo G. (2018) <doi:10.1080/10485252.2018.1531130>. |
License: | MIT + file LICENSE |
Encoding: | UTF-8 |
RoxygenNote: | 7.2.3 |
Imports: | MASS, stats |
Suggests: | testthat (≥ 3.0.0) |
Config/testthat/edition: | 3 |
NeedsCompilation: | no |
Packaged: | 2023-05-15 13:45:50 UTC; Administrator |
Author: | Chunjie Wei [aut, cre], Guangbao Guo [aut] |
Maintainer: | Chunjie Wei <wcj1236@163.com> |
Depends: | R (≥ 3.5.0) |
Repository: | CRAN |
Date/Publication: | 2023-05-15 14:10:02 UTC |
Cloud
Description
A data frame with 1024 observations on the following 10 variables.
Usage
data("Cloud")
Format
A data frame with 1024 observations on the following 10 variables.
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 sets we propose to analyse are constituted of 1024 vectors, each vector includes 10 parameters. You can think of it as a 1024*10 matrix.
Source
The Cloud data set comes from the UCI database.
References
NA
Examples
data(Cloud)
## maybe str(Cloud) ; plot(Cloud) ...
HTRU
Description
A data frame with 10000 observations on the following 9 variables.
Usage
data("HTRU")
Format
A data frame with 10000 observations on the following 9 variables.
A
a numeric vector
B
a numeric vector
C
a numeric vector
D
a numeric vector
E
a numeric vector
F
a numeric vector
G
a numeric vector
H
a numeric vector
I
a numeric vector
Details
Pulsar candidates collected during the HTRU survey. Pulsars are a type of star, of considerable scientific interest. Candidates must be classified in to pulsar and non-pulsar classes to aid discovery.
Source
The HTRU data set comes from the UCI database.
References
NA
Examples
data(HTRU)
## maybe str(HTRU) ; plot(HTRU) ...
The incremental principal component method can handle online data sets.
Description
The incremental principal component method can handle online data sets.
Usage
IPC(data, m, eta)
Arguments
data |
is an online data set |
m |
is the number of principal component |
eta |
is the proportion of online data to total data |
Value
T2,T2k,V,Vhat,lambdahat,time
Examples
library(MASS)
n=2000;p=20;m=9;
mu=t(matrix(rep(runif(p,0,1000),n),p,n))
mu0=as.matrix(runif(m,0))
sigma0=diag(runif(m,1))
F=matrix(mvrnorm(n,mu0,sigma0),nrow=n)
A=matrix(runif(p*m,-1,1),nrow=p)
D=as.matrix(diag(rep(runif(p,0,1))))
epsilon=matrix(mvrnorm(n,rep(0,p),D),nrow=n)
data=mu+F%*%t(A)+epsilon
IPC(data=data,m=m,eta=0.8)
The perturbation principal component method can handle online data sets.
Description
The perturbation principal component method can handle online data sets.
Usage
PPC(data, m, eta)
Arguments
data |
is an online data set |
m |
is the number of principal component |
eta |
is the proportion of online data to total data |
Value
T2,T2k,V,Vhat,lambdahat,time
Examples
library(MASS)
n=2000;p=20;m=9;
mu=t(matrix(rep(runif(p,0,1000),n),p,n))
mu0=as.matrix(runif(m,0))
sigma0=diag(runif(m,1))
F=matrix(mvrnorm(n,mu0,sigma0),nrow=n)
A=matrix(runif(p*m,-1,1),nrow=p)
D=as.matrix(diag(rep(runif(p,0,1))))
epsilon=matrix(mvrnorm(n,rep(0,p),D),nrow=n)
data=mu+F%*%t(A)+epsilon
PPC(data=data,m=m,eta=0.8)
The stochastic approximate component method can handle online data sets.
Description
The stochastic approximate component method can handle online data sets.
Usage
SAPC(data, m, eta, alpha)
Arguments
data |
is a online data set |
m |
is the number of principal component |
eta |
is the proportion of online data to total data |
alpha |
is the step size |
Value
T2,T2k,V,Vhat,lambdahat,time
Examples
library(MASS)
n=2000;p=20;m=9;
mu=t(matrix(rep(runif(p,0,1000),n),p,n))
mu0=as.matrix(runif(m,0))
sigma0=diag(runif(m,1))
F=matrix(mvrnorm(n,mu0,sigma0),nrow=n)
A=matrix(runif(p*m,-1,1),nrow=p)
D=as.matrix(diag(rep(runif(p,0,1))))
epsilon=matrix(mvrnorm(n,rep(0,p),D),nrow=n)
data=mu+F%*%t(A)+epsilon
SAPC(data=data,m=m,eta=0.8,alpha=1)
Wine
Description
A data frame with 177 observations on the following 13 variables.
Usage
data("Wine")
Format
A data frame with 177 observations on the following 13 variables.
X14.23
a numeric vector
X1.71
a numeric vector
X2.43
a numeric vector
X15.6
a numeric vector
X127
a numeric vector
X2.8
a numeric vector
X3.06
a numeric vector
X.28
a numeric vector
X2.29
a numeric vector
X5.64
a numeric vector
X1.04
a numeric vector
X3.92
a numeric vector
X1065
a numeric vector
Details
These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.
Source
The Wine data set comes from the UCI database.
References
NA
Examples
data(Wine)
## maybe str(Wine) ; plot(Wine) ...