Parte 5 informe final
Natalia De la cruz Collazos
2024-06-01
K-Medois
Paquetes
#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## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaData Set
data(poison)
head(poison[, 1:7], 3)## Age Time Sick Sex Nausea Vomiting Abdominals
## 1 9 22 Sick_y F Nausea_y Vomit_n Abdo_y
## 2 5 0 Sick_n F Nausea_n Vomit_n Abdo_n
## 3 6 16 Sick_y F Nausea_n Vomit_y Abdo_ypoison.active <- poison[1:55, 5:15]
head(poison.active[, 1:6], 3)## Nausea Vomiting Abdominals Fever Diarrhae Potato
## 1 Nausea_y Vomit_n Abdo_y Fever_y Diarrhea_y Potato_y
## 2 Nausea_n Vomit_n Abdo_n Fever_n Diarrhea_n Potato_y
## 3 Nausea_n Vomit_y Abdo_y Fever_y Diarrhea_y Potato_y5.2.3 Resumen de datos
# Summary of the 4 first variables
summary(poison.active)[, 1:4]## Nausea Vomiting Abdominals Fever
## Nausea_n:43 Vomit_n:33 Abdo_n:18 Fever_n:20
## Nausea_y:12 Vomit_y:22 Abdo_y:37 Fever_y:35for (i in 1:4) {
plot(poison.active[,i], main=colnames(poison.active)[i],
ylab = "Count", col="steelblue", las = 2)
}
5.24 Codigo R
res.mca <- MCA(poison.active, graph = FALSE)print(res.mca)## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 55 individuals, described by 11 variables
## *The results are available in the following objects:
##
## name description
## 1 "$eig" "eigenvalues"
## 2 "$var" "results for the variables"
## 3 "$var$coord" "coord. of the categories"
## 4 "$var$cos2" "cos2 for the categories"
## 5 "$var$contrib" "contributions of the categories"
## 6 "$var$v.test" "v-test for the categories"
## 7 "$var$eta2" "coord. of variables"
## 8 "$ind" "results for the individuals"
## 9 "$ind$coord" "coord. for the individuals"
## 10 "$ind$cos2" "cos2 for the individuals"
## 11 "$ind$contrib" "contributions of the individuals"
## 12 "$call" "intermediate results"
## 13 "$call$marge.col" "weights of columns"
## 14 "$call$marge.li" "weights of rows"5.3 Visualización e interpretación
Varianzas
library("factoextra")
eig.val <- get_eigenvalue(res.mca)
# head(eig.val)fviz_screeplot(res.mca, addlabels = TRUE, ylim = c(0, 45))
5.3.2 Biplot
fviz_mca_biplot(res.mca,
repel = TRUE, # Avoid text overlapping (slow if many point)
ggtheme = theme_minimal())
5.3.3 Gráfico de variables
var <- get_mca_var(res.mca)
var## Multiple Correspondence Analysis Results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for categories"
## 2 "$cos2" "Cos2 for categories"
## 3 "$contrib" "contributions of categories"# Coordinates
head(var$coord)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## Nausea_n 0.2673909 0.12139029 -0.265583253 0.03376130 0.07370500
## Nausea_y -0.9581506 -0.43498187 0.951673323 -0.12097801 -0.26410958
## Vomit_n 0.4790279 -0.40919465 0.084492799 0.27361142 0.05245250
## Vomit_y -0.7185419 0.61379197 -0.126739198 -0.41041713 -0.07867876
## Abdo_n 1.3180221 -0.03574501 -0.005094243 -0.15360951 -0.06986987
## Abdo_y -0.6411999 0.01738946 0.002478280 0.07472895 0.03399075# Cos2: quality on the factore map
head(var$cos2)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## Nausea_n 0.2562007 0.0528025759 2.527485e-01 0.004084375 0.019466197
## Nausea_y 0.2562007 0.0528025759 2.527485e-01 0.004084375 0.019466197
## Vomit_n 0.3442016 0.2511603912 1.070855e-02 0.112294813 0.004126898
## Vomit_y 0.3442016 0.2511603912 1.070855e-02 0.112294813 0.004126898
## Abdo_n 0.8451157 0.0006215864 1.262496e-05 0.011479077 0.002374929
## Abdo_y 0.8451157 0.0006215864 1.262496e-05 0.011479077 0.002374929# Contributions to the principal components
head(var$contrib)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## Nausea_n 1.515869 0.81100008 4.670018e+00 0.08449397 0.48977906
## Nausea_y 5.431862 2.90608363 1.673423e+01 0.30277007 1.75504164
## Vomit_n 3.733667 7.07226253 3.627455e-01 4.25893721 0.19036376
## Vomit_y 5.600500 10.60839380 5.441183e-01 6.38840581 0.28554563
## Abdo_n 15.417637 0.02943661 7.192511e-04 0.73219636 0.18424268
## Abdo_y 7.500472 0.01432051 3.499060e-04 0.35620363 0.08963157fviz_mca_var(res.mca, choice = "mca.cor",
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())
head(round(var$coord, 2), 4)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## Nausea_n 0.27 0.12 -0.27 0.03 0.07
## Nausea_y -0.96 -0.43 0.95 -0.12 -0.26
## Vomit_n 0.48 -0.41 0.08 0.27 0.05
## Vomit_y -0.72 0.61 -0.13 -0.41 -0.08fviz_mca_var(res.mca,
repel = TRUE, # Avoid text overlapping (slow)
ggtheme = theme_minimal())
fviz_mca_var(res.mca, col.var="black", shape.var = 15,
repel = TRUE)
head(var$cos2, 4)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## Nausea_n 0.2562007 0.05280258 0.25274850 0.004084375 0.019466197
## Nausea_y 0.2562007 0.05280258 0.25274850 0.004084375 0.019466197
## Vomit_n 0.3442016 0.25116039 0.01070855 0.112294813 0.004126898
## Vomit_y 0.3442016 0.25116039 0.01070855 0.112294813 0.004126898# Color by cos2 values: quality on the factor map
fviz_mca_var(res.mca, col.var = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping
ggtheme = theme_minimal())
# Change the transparency by cos2 values
fviz_mca_var(res.mca, alpha.var="cos2",
repel = TRUE,
ggtheme = theme_minimal())
library("corrplot")## Warning: package 'corrplot' was built under R version 4.3.3## corrplot 0.92 loadedcorrplot(var$cos2, is.corr=FALSE)
# Cos2 of variable categories on Dim.1 and Dim.2
fviz_cos2(res.mca, choice = "var", axes = 1:2)
head(round(var$contrib,2), 4)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## Nausea_n 1.52 0.81 4.67 0.08 0.49
## Nausea_y 5.43 2.91 16.73 0.30 1.76
## Vomit_n 3.73 7.07 0.36 4.26 0.19
## Vomit_y 5.60 10.61 0.54 6.39 0.29# Contributions of rows to dimension 1
fviz_contrib(res.mca, choice = "var", axes = 1, top = 15)
# Contributions of rows to dimension 2
fviz_contrib(res.mca, choice = "var", axes = 2, top = 15)
# Total contribution to dimension 1 and 2
fviz_contrib(res.mca, choice = "var", axes = 1:2, top = 15)
fviz_mca_var(res.mca, col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # avoid text overlapping (slow)
ggtheme = theme_minimal()
)
# Change the transparency by contrib values
fviz_mca_var(res.mca, alpha.var="contrib",
repel = TRUE, ggtheme = theme_minimal())
ind <- get_mca_ind(res.mca)
ind## Multiple Correspondence Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"# Coordinates of column points
head(ind$coord)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## 1 -0.4525811 -0.26415072 0.17151614 0.01369348 -0.11696806
## 2 0.8361700 -0.03193457 -0.07208249 -0.08550351 0.51978710
## 3 -0.4481892 0.13538726 -0.22484048 -0.14170168 -0.05004753
## 4 0.8803694 -0.08536230 -0.02052044 -0.07275873 -0.22935022
## 5 -0.4481892 0.13538726 -0.22484048 -0.14170168 -0.05004753
## 6 -0.3594324 -0.43604390 -1.20932223 1.72464616 0.04348157# Quality of representation
head(ind$cos2)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## 1 0.34652591 0.1180447167 0.0497683175 0.0003172275 0.0231460846
## 2 0.55589562 0.0008108236 0.0041310808 0.0058126211 0.2148103098
## 3 0.54813888 0.0500176790 0.1379484860 0.0547920948 0.0068349171
## 4 0.74773962 0.0070299584 0.0004062504 0.0051072923 0.0507479873
## 5 0.54813888 0.0500176790 0.1379484860 0.0547920948 0.0068349171
## 6 0.02485357 0.0365775483 0.2813443706 0.5722083217 0.0003637178# Quality of representation
head(ind$cos2)## Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## 1 0.34652591 0.1180447167 0.0497683175 0.0003172275 0.0231460846
## 2 0.55589562 0.0008108236 0.0041310808 0.0058126211 0.2148103098
## 3 0.54813888 0.0500176790 0.1379484860 0.0547920948 0.0068349171
## 4 0.74773962 0.0070299584 0.0004062504 0.0051072923 0.0507479873
## 5 0.54813888 0.0500176790 0.1379484860 0.0547920948 0.0068349171
## 6 0.02485357 0.0365775483 0.2813443706 0.5722083217 0.0003637178fviz_mca_ind(res.mca, col.ind = "cos2",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping (slow if many points)
ggtheme = theme_minimal())
# Cos2 of individuals
fviz_cos2(res.mca, choice = "ind", axes = 1:2, top = 20)
# Contribution of individuals to the dimensions
fviz_contrib(res.mca, choice = "ind", axes = 1:2, top = 20)
5.3.5 Colorear individuos por grupos
fviz_mca_ind(res.mca,
label = "none", # hide individual labels
habillage = "Vomiting", # color by groups
palette = c("#00AFBB", "#E7B800"),
addEllipses = TRUE, ellipse.type = "confidence",
ggtheme = theme_minimal())
# habillage = index of the column to be used as grouping variable
fviz_mca_ind(res.mca, habillage = 2, addEllipses = TRUE)
fviz_mca_ind(res.mca, habillage = poison$Vomiting, addEllipses = TRUE)
fviz_ellipses(res.mca, c("Vomiting", "Fever"),
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.
fviz_ellipses(res.mca, 1:4, geom = "point")
5.3.6 Descripción de la dimensión
res.desc <- dimdesc(res.mca, axes = c(1,2))
# Description of dimension 1
res.desc[[1]]##
## Link between the variable and the categorical variable (1-way anova)
## =============================================
## R2 p.value
## Abdominals 0.8451157 4.055640e-23
## Diarrhae 0.7994680 3.910776e-20
## Fever 0.7846788 2.600566e-19
## Mayo 0.3829749 4.756234e-07
## Vomiting 0.3442016 2.510738e-06
## Nausea 0.2562007 8.062777e-05
## Cheese 0.1944181 7.534834e-04
##
## Link between variable and the categories of the categorical variables
## ================================================================
## Estimate p.value
## Abdominals=Abdo_n 0.5671866 4.055640e-23
## Diarrhae=Diarrhea_n 0.5380920 3.910776e-20
## Fever=Fever_n 0.5330918 2.600566e-19
## Mayo=Mayo_n 0.4644981 4.756234e-07
## Vomiting=Vomit_n 0.3466915 2.510738e-06
## Nausea=Nausea_n 0.3547892 8.062777e-05
## Cheese=Cheese_n 0.3830043 7.534834e-04
## Cheese=Cheese_y -0.3830043 7.534834e-04
## Nausea=Nausea_y -0.3547892 8.062777e-05
## Vomiting=Vomit_y -0.3466915 2.510738e-06
## Mayo=Mayo_y -0.4644981 4.756234e-07
## Fever=Fever_y -0.5330918 2.600566e-19
## Diarrhae=Diarrhea_y -0.5380920 3.910776e-20
## Abdominals=Abdo_y -0.5671866 4.055640e-23# Description of dimension 2
res.desc[[2]]##
## Link between the variable and the categorical variable (1-way anova)
## =============================================
## R2 p.value
## Courgette 0.4464145 2.500166e-08
## Potato 0.3957543 2.690662e-07
## Vomiting 0.2511604 9.728027e-05
## Icecream 0.1409011 4.743927e-03
##
## Link between variable and the categories of the categorical variables
## ================================================================
## Estimate p.value
## Courgette=Courg_n 0.4176013 2.500166e-08
## Potato=Potato_y 0.4977523 2.690662e-07
## Vomiting=Vomit_y 0.1838104 9.728027e-05
## Icecream=Icecream_n 0.2597197 4.743927e-03
## Icecream=Icecream_y -0.2597197 4.743927e-03
## Vomiting=Vomit_n -0.1838104 9.728027e-05
## Potato=Potato_n -0.4977523 2.690662e-07
## Courgette=Courg_y -0.4176013 2.500166e-085.4 Elementos complementarios
5.4.3 Resultados
# Supplementary qualitative variable categories
res.mca$quali.sup## NULL# Supplementary quantitative variables
res.mca$quanti## NULL# Supplementary individuals
res.mca$ind.sup## NULL5.4.4 Parcelas
# Biplot of individuals and variable categories
fviz_mca_biplot(res.mca, repel = TRUE,
ggtheme = theme_minimal())
fviz_mca_var(res.mca, choice = "mca.cor",
repel = TRUE)
fviz_mca_var(res.mca, repel = TRUE,
ggtheme= theme_minimal())
fviz_mca_ind(res.mca,
label = "ind.sup", #Show the label of ind.sup only
ggtheme = theme_minimal())
5.5 Filtrado de resultados
# Visualize variable categories with cos2 >= 0.4
fviz_mca_var(res.mca, select.var = list(cos2 = 0.4))
# Top 10 active variables with the highest cos2
fviz_mca_var(res.mca, select.var= list(cos2 = 10))
# Select by names
name <- list(name = c("Fever_n", "Abdo_y", "Diarrhea_n",
"Fever_Y", "Vomit_y", "Vomit_n"))
fviz_mca_var(res.mca, select.var = name)
# top 5 contributing individuals and variable categories
fviz_mca_biplot(res.mca, select.ind = list(contrib = 5),
select.var = list(contrib = 5),
ggtheme = theme_minimal())
5.6 Exportación de resultados
5.6.1 Exportar gráficos a archivos PDF/PNG
# Scree plot
scree.plot <- fviz_eig(res.mca)
# Biplot of row and column variables
biplot.mca <- fviz_mca_biplot(res.mca)
library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.3.3ggexport(plotlist = list(scree.plot, biplot.mca),
filename = "MCA.pdf")## file saved to MCA.pdf5.6.2 Exportar resultados a archivos txt/csv
# Export into a TXT file
write.infile(res.mca, "mca.txt", sep = "\t")
# Export into a CSV file
write.infile(res.mca, "mca.csv", sep = ";")5.7 Resumen
library("ade4")## Warning: package 'ade4' was built under R version 4.3.3##
## Attaching package: 'ade4'## The following object is masked from 'package:FactoMineR':
##
## reconstres.mca <- dudi.acm(poison.active, scannf = FALSE, nf = 5)
library("ExPosition")## Loading required package: prettyGraphsres.mca <- epMCA(poison.active, graph = FALSE, correction = "bg")fviz_eig(res.mca) # Scree plot
fviz_mca_biplot(res.mca) # Biplot of rows and columns
5.8 Lectura adicional
Para conocer los antecedentes matemáticos detrás de MCA, consulte los siguientes cursos en video, artículos y libros: -Curso de Análisis de Correspondencias con FactoMineR (Videocursos). https://goo.gl/Hhh6hC -Análisis Multivariado Exploratorio por Ejemplo Usando R (libro) (F. Husson, Le, and Pagès 2017). Análisis de componentes principales (artículo) (Abdi y Williams 2010). https://goo.gl/1Vtwq1. -Fundamentos de análisis de correspondencia (entrada de blog). https://goo.gl/Xyk8KT