Parte 6 informe final
Natalia De la cruz Collazos
2024-06-02
Carga de librerias y Datesets
#install.packages(c("FactoMineR", "factoextra"))
library("FactoMineR")## Warning: package 'FactoMineR' was built under R version 4.3.3library("factoextra")## Warning: package 'factoextra' was built under R version 4.3.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.3.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary("FactoMineR")
data(wine)
df <- wine[,c(1,2, 16, 22, 29, 28, 30,31)]
head(df[, 1:7], 4)## Label Soil Plante Acidity Harmony Intensity Overall.quality
## 2EL Saumur Env1 2.000 2.107 3.143 2.857 3.393
## 1CHA Saumur Env1 2.000 2.107 2.964 2.893 3.214
## 1FON Bourgueuil Env1 1.750 2.179 3.143 3.074 3.536
## 1VAU Chinon Env2 2.304 3.179 2.038 2.462 2.464str(df)## 'data.frame': 21 obs. of 8 variables:
## $ Label : Factor w/ 3 levels "Saumur","Bourgueuil",..: 1 1 2 3 1 2 2 1 3 1 ...
## $ Soil : Factor w/ 4 levels "Reference","Env1",..: 2 2 2 3 1 1 1 2 2 3 ...
## $ Plante : num 2 2 1.75 2.3 1.76 ...
## $ Acidity : num 2.11 2.11 2.18 3.18 2.57 ...
## $ Harmony : num 3.14 2.96 3.14 2.04 3.64 ...
## $ Intensity : num 2.86 2.89 3.07 2.46 3.64 ...
## $ Overall.quality: num 3.39 3.21 3.54 2.46 3.74 ...
## $ Typical : num 3.25 3.04 3.18 2.25 3.44 ...library(FactoMineR)
res.famd <- FAMD(df, graph = FALSE)
print(res.famd)## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues and inertia"
## 2 "$var" "Results for the variables"
## 3 "$ind" "results for the individuals"
## 4 "$quali.var" "Results for the qualitative variables"
## 5 "$quanti.var" "Results for the quantitative variables"library("factoextra")
eig.val <- get_eigenvalue(res.famd)
head(eig.val) ## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 4.8315174 43.922886 43.92289
## Dim.2 1.8568797 16.880724 60.80361
## Dim.3 1.5824794 14.386176 75.18979
## Dim.4 1.1491200 10.446546 85.63633
## Dim.5 0.6518053 5.925503 91.561836.3 Visualization and interpretation
La función fviz_eig() o fviz_screeplot() [paquete factoextra] se puede utilizar para dibujar el diagrama de pedregal (los porcentajes de inercia explicados por cada dimensión FAMD):
6.3.1 Eigenvalus / Variances
fviz_screeplot(res.famd)
Graph of variables
6.3.2.1 All variables
var <- get_famd_var(res.famd)
var## FAMD results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates"
## 2 "$cos2" "Cos2, quality of representation"
## 3 "$contrib" "Contributions"head(var$coord)## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Plante 0.7344160 0.060551966 0.105902048 0.004011299 0.0010340559
## Acidity 0.1732738 0.491118153 0.126394029 0.115376784 0.0045862935
## Harmony 0.8943968 0.023628146 0.040124469 0.003653813 0.0086624633
## Intensity 0.6991811 0.134639254 0.065382234 0.023214984 0.0064730431
## Overall.quality 0.9115699 0.005246728 0.009336677 0.005445276 0.0007961880
## Typical 0.7808611 0.027094327 0.001549791 0.083446627 0.0005912942head(var$cos2)## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Plante 0.53936692 3.666541e-03 1.121524e-02 1.609052e-05 1.069272e-06
## Acidity 0.03002381 2.411970e-01 1.597545e-02 1.331180e-02 2.103409e-05
## Harmony 0.79994566 5.582893e-04 1.609973e-03 1.335035e-05 7.503827e-05
## Intensity 0.48885427 1.812773e-02 4.274836e-03 5.389355e-04 4.190029e-05
## Overall.quality 0.83095973 2.752815e-05 8.717353e-05 2.965103e-05 6.339153e-07
## Typical 0.60974400 7.341026e-04 2.401853e-06 6.963340e-03 3.496288e-07head(var$contrib)## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Plante 15.200526 3.2609526 6.69215972 0.3490757 0.15864490
## Acidity 3.586323 26.4485720 7.98708850 10.0404466 0.70362936
## Harmony 18.511716 1.2724651 2.53554453 0.3179662 1.32899551
## Intensity 14.471254 7.2508336 4.13163258 2.0202401 0.99309457
## Overall.quality 18.867156 0.2825562 0.59000304 0.4738648 0.12215119
## Typical 16.161818 1.4591321 0.09793437 7.2617850 0.09071638fviz_famd_var(res.famd, repel = TRUE)
fviz_contrib(res.famd, "var", axes = 1)
fviz_contrib(res.famd, "var", axes = 2)
6.3.2.2 Graph of variables
quanti.var <- get_famd_var(res.famd)
print(quanti.var)## FAMD results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates"
## 2 "$cos2" "Cos2, quality of representation"
## 3 "$contrib" "Contributions"fviz_famd_var(res.famd, "quanti.var", repel = TRUE, col.var = "Pink")
fviz_famd_var(res.famd, "quanti.var", col.var = "contrib",
gradient.cols = c("#F699CD", "#FEC5E5", "#FE7F9C"),
repel = TRUE)
fviz_famd_var(res.famd, "quanti.var", col.var = "cos2",
gradient.cols = c("#F699CD", "#FEC5E5", "#FE7F9C"),
repel = TRUE)
6.3.2.3 Graph of qualitative variables
quali.var <- get_famd_var(res.famd, "quali.var")
print(quali.var)## FAMD results for qualitative variable categories
## ===================================================
## Name Description
## 1 "$coord" "Coordinates"
## 2 "$cos2" "Cos2, quality of representation"
## 3 "$contrib" "Contributions"fviz_famd_var(res.famd, "quali.var", col.var = "contrib", gradient.cols = c("#F699CD", "#FEC5E5", "#FE7F9C"))
6.3.3 Graph of individuals
ind <- get_famd_ind(res.famd)
print(ind)## FAMD results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates"
## 2 "$cos2" "Cos2, quality of representation"
## 3 "$contrib" "Contributions"fviz_famd_ind(res.famd, col.ind = "cos2", gradient.cols = c("#F699CD", "#FEC5E5", "#FE7F9C"), repel = TRUE)
fviz_mfa_ind(res.famd, habillage = "Label", palette = c("#F699CD", "#FC46AA", "#FE7F9C"), addEllipses = TRUE, ellipse.type = "confidence", repel = TRUE)
fviz_mfa_ind(res.famd, habillage = "Label", palette = c("#F699CD", "#FC46AA", "#FE7F9C"), addEllipses = TRUE, ellipse.type = "confidence", repel = TRUE)
fviz_ellipses(res.famd, 1:2, geom = "point")## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
6.4 Summary
La técnica de Análisis Factorial de Múltiples Correspondencias (FAMD) permite examinar conjuntos de datos que contienen variables de naturaleza cualitativa y cuantitativa. En este sentido, se exploró la utilización e interpretación de FAMD utilizando los paquetes FactoMineR y factoextra en R.