The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Type: Package
Title: The Sparse Online Principal Component Estimation Algorithm
Version: 0.1.0
Description: The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Imports: elasticnet, magrittr, stats
Suggests: testthat (≥ 3.0.0)
Config/testthat/edition: 3
Maintainer: Guangbao Guo <ggb11111111@163.com>
NeedsCompilation: no
Author: Guangbao Guo [aut, cre], Chunjie Wei [aut], Guoqi Qian [aut]
Depends: R (≥ 4.1.0)
Repository: CRAN
Packaged: 2023-05-07 04:25:08 UTC; Administrator
Date/Publication: 2023-05-07 04:50:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling 'rhs(lhs)'.

Heart failure

Description

Heart failure

Usage

data("Heart")

Format

A data frame with 299 observations on the following 13 variables.

age

a numeric vector

anaemia

a numeric vector

creatinine_phosphokinase

a numeric vector

diabetes

a numeric vector

ejection_fraction

a numeric vector

high_blood_pressure

a numeric vector

platelets

a numeric vector

serum_creatinine

a numeric vector

serum_sodium

a numeric vector

sex

a numeric vector

smoking

a numeric vector

time

a numeric vector

DEATH_EVENT

a numeric vector

Details

This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features.

Source

The Heart failure data set comes from the UCI database.

References

Davide Chicco, Giuseppe Jurman. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making.

Examples

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

Hugging

Description

The EMG Physical Action-Hugging data set.

Usage

data("Hugging")

Format

A data frame with 9752 observations on the following 8 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

Details

The data set is a body movement data set, including 10 normal and 10 aggressive body movements. The data frame with 9752 observations on the following 8 variables.

Source

The Hugging data set comes from the UCI database.

References

Demir et al. (2019). Surface emg signals and deep transfer learning-based physical action classification. Neural Computing and Applications.

Examples

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

The incremental principal component can handle online data sets with highly correlated.

Description

The incremental principal component can handle online data sets with highly correlated.

Usage

IPC(data, m, eta)

Arguments

data

is a highly correlated online data set

m

is the number of principal component

eta

is the proportion of online data to total data

Value

Ai,Di

Examples

IPC(data=PSA,m=3,eta=0.8)

The online principal component method refers to the IPC method with the best performance among the IPC, the PPC and the SAPC methods.

Description

The online principal component method refers to the IPC method with the best performance among the IPC, the PPC and the SAPC methods.

Usage

OPC(data, m, eta)

Arguments

data

is a highly correlated online data set

m

is the number of principal component

eta

is the proportion of online data to total data

Value

Ao,Do

Examples

OPC(data=PSA,m=3,eta=0.8)

The traditional principal component method. This method can estimate the eigen space of the data set.

Description

The traditional principal component method. This method can estimate the eigen space of the data set.

Usage

PC(data, m = m)

Arguments

data

is a highly correlated data set

m

is the number of principal component

Value

Ahat, Dhat

Examples

PC(data=PSA,m=3)

The perturbation principal component can handle online data sets with highly correlated.

Description

The perturbation principal component can handle online data sets with highly correlated.

Usage

PPC(data, m, eta)

Arguments

data

is a highly correlated online data set

m

is the number of principal component

eta

is the proportion of online data to total data

Value

Ap,Dp

Examples

PPC(data=PSA,m=3,eta=0.8)

Prostate Specific Antigen

Description

The prostate specific antigen (PSA) data set.

Usage

data("PSA")

Format

lcavol

a numeric vector

lweight

a numeric vector

age

a numeric vector

lbph

a numeric vector

svi

a numeric vector

lcp

a numeric vector

gleason

a numeric vector

pgg45

a numeric vector

lpsa

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(PSA)
## maybe str(PSA) ; plot(PSA) ...

The stochastic approximation principal component can handle online data sets with highly correlated.

Description

The stochastic approximation principal component can handle online data sets with highly correlated.

Usage

SAPC(data, m, eta)

Arguments

data

is a highly correlated online data set

m

is the number of principal component

eta

is the proportion of online data to total data

Value

Asa,Dsa

Examples

SAPC(data=PSA,m=3,eta=0.8)

The sparse online principal component can not only process online data sets, but also obtain a sparse solution of online data sets.

Description

The sparse online principal component can not only process online data sets, but also obtain a sparse solution of online data sets.

Usage

SOPC(data, m, gamma, eta)

Arguments

data

is a highly correlated online data set

m

is the number of principal component

gamma

is a sparse parameter

eta

is the proportion of online data to total data

Value

Aso,Dso

Examples

require(elasticnet)
SOPC(PSA,3,0.03,0.6)

The sparse principal component can obtain sparse solutions of the eigenmatrix to better explain the relationship between principal components and original variables.

Description

The sparse principal component can obtain sparse solutions of the eigenmatrix to better explain the relationship between principal components and original variables.

Usage

SPC(data, m, gamma)

Arguments

data

is a highly correlated data set

m

is the number of principal component

gamma

is a sparse parameter

Value

As,Ds

Examples

require(elasticnet)
SPC(data=PSA,m=3,gamma=0.03)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.