2008
DOI: 10.18637/jss.v025.i01
|View full text |Cite
|
Sign up to set email alerts
|

FactoMineR: AnRPackage for Multivariate Analysis

Abstract: In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). Moreover, the dimensions issued from the different exploratory data analy… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

5
4,141
0
96

Year Published

2013
2013
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 7,730 publications
(4,700 citation statements)
references
References 7 publications
5
4,141
0
96
Order By: Relevance
“…All quantitative variables were transformed into categorical variables by defining classes, considered as meaningful on the basis of qualitative information collected during the survey and the resulting repartition of the total sample. A multiple correspondence factorial analysis (MCA) was performed on the 11 variables with the R package FactoMineR (Lê et al 2008). All variables with modalities showing insufficient contribution to the MCA axes (i.e.…”
Section: Resultsmentioning
confidence: 99%
“…All quantitative variables were transformed into categorical variables by defining classes, considered as meaningful on the basis of qualitative information collected during the survey and the resulting repartition of the total sample. A multiple correspondence factorial analysis (MCA) was performed on the 11 variables with the R package FactoMineR (Lê et al 2008). All variables with modalities showing insufficient contribution to the MCA axes (i.e.…”
Section: Resultsmentioning
confidence: 99%
“…O software R v2.12.1 (IHAKA; GENTLEMAN, 1996) foi utilizado para realizar a ACP a partir da matriz de covariância das variáveis e para aplicar a técnica de agrupamento hierárquico baseada em componentes principais (HCPC), ambas funções do pacote FactoMineR (LÊ et al, 2008). Conforme recomenda McKenzie (2005), os histogramas das 30 variáveis padronizadas, como uma aproximação para suas funções densidade de probabilidade, foram observados, o que permitiu verificar a inexistência de potenciais problemas de clumping (agregado de grupos) e truncation (intervalos de valores que não permitem diferenciar grupos), que podem ser associados às análises exploratórias baseadas em ACP.…”
Section: Análise Dos Dadosunclassified
“…We grouped the variables into six themes: (1) climate change perceptions, (2) crop species cultivated, (3) crop and variety richness, (4) crops perceived as susceptible to climate change stresses, (5) crops perceived as tolerant to climate change stresses, and (6) adaptation actions. The themes were balanced in the global analysis of the MFA, which weighs them by the inverse of the first eigenvalue of the separate analysis tables (Lê et al 2008). Additional variables were included in the analysis as supplementary information that did not contribute to the formation of the MFA axes or the clustering but which were correlated to the results.…”
Section: Resultsmentioning
confidence: 99%
“…Following a similar approach to that applied by Choisis et al (2012) to analyse farming system diversity, we performed hierarchical clustering on the first nine axes of the component scores of the MFA (which captured 50% of the variance) using Ward's criterion. The analysis was performed using the FactoMineR package (Lê et al 2008) in the R environment (R version 3.03; R Development Core Team 2008).…”
Section: Resultsmentioning
confidence: 99%