Title:	The Iterated Score Regression-Based Estimation
Date:	2025-05-16
Version:	2025.5.16
Description:	We use the ISR to handle with PCA-based missing data with high correlation, and the DISR to handle with distributed PCA-based missing data. The philosophy of the package is described in Guo G. (2024) <doi:10.1080/03610918.2022.2091779>.
Encoding:	UTF-8
License:	MIT + file LICENSE
LazyData:	true
RoxygenNote:	7.3.2
Imports:	MASS, stats
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
Depends:	R (≥ 3.5.0)
NeedsCompilation:	no
Packaged:	2025-05-16 09:21:11 UTC; Administrator
Author:	Guangbao Guo [aut, cre], Haoyue Song [aut], Lixing Zhu [aut]
Maintainer:	Guangbao Guo <ggb11111111@163.com>
Repository:	CRAN
Date/Publication:	2025-05-16 09:40:20 UTC

CKD

Description

chronic kidney disease

Usage

data("CKD")

Format

The format is: num [1:400, 1:18] 48 7 62 48 51 60 68 24 52 53 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:18] "age" "bp" "sg" "al" ...

Details

There are 1010 missing values in the data set, accounting for 14.03 percent.

Source

Dr.P.Soundarapandian.M.D.,D.M (Senior Consultant Nephrologist), Apollo Hospitals, Managiri, Madurai Main Road, Karaikudi, Tamilnadu, Indi

References

Polat, H., Danaei-Mehr, H., and Cetin, A. (2017). Diagnosis of chronic kidney disease based on support vector machine by feature selection methods. Journal of Medical Systems, 41(4), 1-11.

Examples

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

Caculate the estimator with the DISR method

Description

Caculate the estimator with the DISR method

Usage

DISR(data, data0, real = TRUE, example = FALSE, D)

Arguments

Value

Examples

 library(MASS)
 n=100;p=10;per=0.1
 X0=data=matrix(mvrnorm(n*p,0,1),n,p)
 m=round(per*n*p,digits=0)
 mr=sample(1:(n*p),m,replace=FALSE)
 X0[mr]=NA;data0=X0
 DISR(data=data,data0=data0,real=FALSE,example=FALSE,D=2)

HCV

Description

Hepatitis C virus

Usage

data("HCV")

Format

The format is: num [1:615, 1:13] 1 1 1 1 1 1 1 1 1 1 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:615] "1" "2" "3" "4" ... ..$ : chr [1:13] "Category" "Age" "Sex" "ALB" ...

Details

There are 31 missing values in the data set, accounting for 0.39 percent.

Source

UCI repository

References

Lichtinghagen, R., Pietsch, D., Bantel, H., Manns, M., Brand, K. and Bahr, Matthias. (2013). The Enhanced Liver Fibrosis (ELF) Score: Normal Values, Influence Factors and Proposed Cut-Off Values.. Journal of hepatology. 59. 236-242.

Examples

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

Caculate the estimator with the ISR method

Description

Caculate the estimator with the ISR method

Usage

ISR(data, data0, real = TRUE, example = FALSE)

Arguments

Value

Examples

 library(MASS)
 n=100;p=10;per=0.1
 X0=data=matrix(mvrnorm(n*p,0,1),n,p)
 m=round(per*n*p,digits=0)
 mr=sample(1:(n*p),m,replace=FALSE)
 X0[mr]=NA;data0=X0
 ISR(data=data,data0=data0,real=FALSE,example=FALSE)

Caculate the estimator on the MMLPCA method

Description

Caculate the estimator on the MMLPCA method

Usage

MMLPCA(data, data0, real = TRUE, example = FALSE)

Arguments

Value

Examples

 library(MASS)
 n=100;p=10;per=0.1
 X0=data=matrix(mvrnorm(n*p,0,1),n,p)
 m=round(per*n*p,digits=0)
 mr=sample(1:(n*p),m,replace=FALSE)
 X0[mr]=NA;data0=X0
 MMLPCA(data=data,data0=data0,real=FALSE,example=FALSE)

Caculate the estimator on the MNIPALS method

Description

Caculate the estimator on the MNIPALS method

Usage

MNIPALS(data, data0, real = TRUE, example = FALSE)

Arguments

Value

Examples

 library(MASS)
 n=100;p=10;per=0.1
 X0=data=matrix(mvrnorm(n*p,0,1),n,p)
 m=round(per*n*p,digits=0)
 mr=sample(1:(n*p),m,replace=FALSE)
 X0[mr]=NA;data0=X0
 MNIPALS(data=data,data0=data0,real=FALSE,example=FALSE)

Caculate the estimator on the MRPCA method

Description

Caculate the estimator on the MRPCA method

Usage

MRPCA(data, data0, real = TRUE, example = FALSE)

Arguments

Value

Examples

 library(MASS)
 library(MASS)
 n=100;p=10;per=0.1
 X0=data=matrix(mvrnorm(n*p,0,1),n,p)
 m=round(per*n*p,digits=0)
 mr=sample(1:(n*p),m,replace=FALSE)
 X0[mr]=NA;data0=X0
 MRPCA(data=data,data0=data0,real=FALSE,example=FALSE)

Caculate the estimator on the Mean method

Description

Caculate the estimator on the Mean method

Usage

Mean(data, data0, real = TRUE, example = FALSE)

Arguments

Value

Examples

 library(MASS)
 n=100;p=10;per=0.1
 X0=data=matrix(mvrnorm(n*p,0,1),n,p)
 m=round(per*n*p,digits=0)
 mr=sample(1:(n*p),m,replace=FALSE)
 X0[mr]=NA;data0=X0
 Mean(data=data,data0=data0,real=FALSE,example=FALSE)

PM2.5

Description

Beijing PM2.5

Usage

data("PM2.5")

Format

The format is: num [1:43824, 1:12] 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:43824] "1" "2" "3" "4" ... ..$ : chr [1:12] "year" "month" "day" "hour" ...

Details

It records 43824 daily measurements on 12 variables and there are 2067 missing values on 2067 measurements, accounting for 0.00393.

Source

UCI repository

References

X. Liang, T. Zou, B. Guo, S. Li, H. Zhang, S. Zhang, H. Huang, and S. Chen. Assessing Beijing's PM2.5 pollution: severity, weather impact, APEC and winter heating. Proceedings of the Royal Society A, 471(2182):1–20, 2015.

Examples

data(PM2.5)
## maybe str(PM2.5) ; plot(PM2.5) ...

Caculate the estimator on the SR method

Description

Caculate the estimator on the SR method

Usage

SR(data, data0, real = TRUE, example = FALSE)

Arguments

Value

Examples

 library(MASS)
 n=100;p=10;per=0.1
 X0=data=matrix(mvrnorm(n*p,0,1),n,p)
 m=round(per*n*p,digits=0)
 mr=sample(1:(n*p),m,replace=FALSE)
 X0[mr]=NA;data0=X0
 SR(data=data,data0=data0,real=FALSE,example=FALSE)

orange

Description

orange

Usage

data("orange")

Format

The format is: num [1:12, 1:8] 4.79 4.58 4.71 6.58 NA ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:12] "1" "2" "3" "4" ... ..$ : chr [1:8] "Color.intensity" "Odor.intensity" "Attack.intensity" "Sweet" ...

Details

There are 19 missing values in the data set, accounting for 19.79 percent.

Source

http://factominer.free.fr/missMDA/index.html

References

Josse J, Husson F (2016). missMDA: A Package for Handling Missing Values in Multivariate Data Analysis. Journal of Statistical Software, 70(1), 1–31.

Examples

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

ozone

Description

ozone

Usage

data("ozone")

Format

A data frame with 112 observations on the following 11 variables.

maxO3

a numeric vector

T9

a numeric vector

T12

a numeric vector

T15

a numeric vector

Ne9

a numeric vector

Ne12

a numeric vector

Ne15

a numeric vector

Vx9

a numeric vector

Vx12

a numeric vector

Vx15

a numeric vector

maxO3v

a numeric vector

Details

There are 115 missing values in it, accounting for 9.96 percent.

Source

http://factominer.free.fr/missMDA/index.html

References

Audigier, V., Husson, F., and Josse, J. (2014). A principal components method to impute missing values for mixed data. Advances in Data Analysis and Classification, 10(1), 5-26.

Examples

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

review

Description

Travel reviews

Usage

data("review")

Format

The format is: num [1:980, 1:10] 0.93 1.02 1.22 0.45 0.51 0.99 0.9 0.74 1.12 0.7 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:980] "User_1" "User_2" "User_3" "User_4" ... ..$ : chr [1:10] "Category_1" "Category_2" "Category_3" "Category_4" ...

Details

980 travelers' reviews of 10 different types of travel facilities in East Asia

Source

UCI repository

References

Renjith, S., Sreekumar, A., and Jathavedan, M. (2018). Evaluation of partitioning clustering algorithms for processing social media data in tourism domain. 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 127-131.

Examples

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