educFM

<p>Education level of father (F) and mother (M) in percentages of low
(l), medium (m), and high (h) of 31 countries in Europe.</p>

data

Methods for analysis of compositional data including robust
methods  (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace
rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>),
count zeros (<doi:10.1177/1471082X14535524>),
methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier
detection for compositional data, (robust) principal component analysis for
compositional data, (robust) factor analysis for compositional data, (robust)
discriminant analysis for compositional data (Fisher rule), robust regression
with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>),
contingency (<doi:10.1080/03610926.2013.824980>)
and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>)
and (robust) Anderson-Darling normality tests for
compositional data as well as popular log-ratio transformations (addLR, cenLR,
isomLR, and their inverse transformations). In addition, visualisation and
diagnostic tools are implemented as well as high and low-level plot functions
for the ternary diagram.

Matthias Templ

robCompositions

Compositional Data Analysis

Karel Hron

Peter Filzmoser

Kamila Facevicova

Petra Kynclova

Jan Walach

Veronika Pintar

Jiajia Chen

Dominika Miksova

Bernhard Meindl

Alessandra Menafoglio

Alessia Di Blasi

Federico Pavone

Nikola Stefelova

Gianluca Zeni

Roman Wiederkehr

educFM function

<p>A data frame with 31 observations and 8 variables</p>

Format

Author

education level of father (F) and mother (M) — educFM

education level of father (F) and mother (M)

educFM: education level of father (F) and mother (M)

Description

Usage

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Author

Details

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