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robustfa
View on CRAN: Click
here
Download and install robustfa package within the R console
Install from CRAN:
install.packages("robustfa")
Install from Github:
library("remotes")
install_github("cran/robustfa")
Install by package version:
library("remotes")
install_version("robustfa", "1.1-0")
Attach the package and use:
library("robustfa")
Maintained by
Frederic Bertrand
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.robustfa . robustfa results . robustfa.pdf . robustfa_1.1-0.tar.gz . robustfa_1.1-0.zip . robustfa_1.1-0.zip . robustfa_1.1-0.zip . robustfa_1.1-0.tgz . robustfa_1.1-0.tgz . robustfa_1.1-0.tgz . robustfa_1.1-0.tgz . robustfa_1.1-0.tgz . robustfa_1.1-0.tgz . robustfa archive . https://CRAN.R-project.org/package=robustfa .
First Published: 2012-03-12
Latest Update: 2023-04-16
Description:
Outliers virtually exist in any datasets of any application field.
To avoid the impact of outliers, we need to use robust estimators.
Classical estimators of multivariate mean and covariance matrix are
the sample mean and the sample covariance matrix. Outliers will
affect the sample mean and the sample covariance matrix, and thus
they will affect the classical factor analysis which depends on
the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser,
P. and Croux, C. (2003) ).
So it is necessary to use the robust estimators of the sample
mean and the sample covariance matrix. There are several robust
estimators in the literature: Minimum Covariance Determinant estimator,
Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid,
M, S, and Stahel-Donoho.
The most direct way to make multivariate analysis more robust is to replace
the sample mean and the sample covariance matrix of the classical estimators
to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006)
) (Todorov, V. and Filzmoser, P. (2009)
), which is our choice of robust factor
analysis. We created an object oriented solution for robust factor
analysis based on new S4 classes.
How to cite:
Frederic Bertrand (2012). robustfa: Object Oriented Solution for Robust Factor Analysis. R package version 1.1-0, https://cran.r-project.org/web/packages/robustfa. Accessed 26 Mar. 2025.
Previous versions and publish date:
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Complete documentation for robustfa
Functions, R codes and Examples using
the robustfa R package
Some associated functions: Fa-class . FaClassic-class . FaClassic . FaCov-class . FaCov . FaRobust-class . SummaryFa-class . Ulogical-class . Unumeric-class . computeScores . compute_cov_cor . detail . factorScorePca . factorScorePfa . fsOrder . getCenter-methods . getEigenvalues-methods . getFa-methods . getLoadings-methods . getQuan-methods . getScores-methods . getSdev-methods . myFaPrint . myplotDD . plot-methods . predict-methods . print-methods . robustfa-package . stock611 . summary-methods .
Some associated R codes: AllClasses.R . AllGenerics.R . Fa.R . FaClassic.R . FaCov.R . factorScorePca.R . factorScorePfa.R . fsOrder.R . plot-utils.R . robustfa.R . utils.R . Full robustfa package functions and examples
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