Multiple factor analysis of mixed data

Description

Performs multiple factor analysis to analyze a set of individuals (observations) described by several groups of variables. Variables within a group can be a mixture of quantitative and qualitative variables.

Usage

  MFAmix(data, groups, name.groups, ndim=5, rename.level=FALSE, graph = TRUE,
    axes = c(1, 2))

Arguments

Details

Multiple Factor Analysis (MFA) developed by Escofier and Pages in 1983 is a method of factorial analysis to deal with multiple groups of variables collected on the same observations. The main idea of MFA is to normalize each group by dividing all the variables belonging to this group by the first eigenvalue coming from the Principal Component Analysis (PCA) of this group. Then, a usual PCA on all the weighted variables taken together is applied. Initially this method has been developed for groups only containing quantitative variables. Afterwards this method has been improved to deal simultaneously with groups of qualitative variables and groups of quantitative variables. The MFAmix method allows to perform MFA method for groups containing a mixture of quantitative and qualitative variables

One of the outputs available in the MFAmix method are the squared loadings (sqload). Squared loadings for a qualitative variable are correlation ratios between the variable and the principal components. For a quantitative variable, squared loadings are the squared correlation between the variable and the principal components.

Some others outputs are specific to MFA:

• Coordinates of groups are the sum of the absolute contributions of variables belonging to the groups,

• Partial individuals coordinates are factor coordinates of individuals according to a specific group. The partial coordinates can be achieved by projecting the data set of each group onto the principal component space of MFAmix,

• Partial axes of a group are correlation between each principal components of the separated analyses of the group and the principal components of MFAmix.

Value

Author(s)

Amaury Labenne amaury.labenne@irstea.fr, Marie Chavent, Vanessa Kuentz, Benoit Liquet, Jerome Saracco

References

Chavent M., Kuentz-Simonet V., Labenne A., Saracco J., Multivariate analysis of mixed data: The PCAmixdata R package, arXiv:1411.4911 [stat.CO].

Escofier, B. and Pages, J. (1994). Multiple factor analysis (afmult package). Computational statistics & data analysis, 18(1):121-140.

Le, S., Josse, J., and Husson, F. (2008). Factominer: an r package for multivariate analysis. Journal of statistical software, 25(1):1-18.

See Also

print.MFAmix, summary.MFAmix, predict.MFAmix, plot.MFAmix

Examples

data(gironde)

class.var<-c(rep(1,9),rep(2,5),rep(3,9),rep(4,4))
names <- c("employment","housing","services","environment")

dat<-cbind(gironde$employment[1:20,],gironde$housing[1:20,],
      gironde$services[1:20,],gironde$environment[1:20,])
      
res<-MFAmix(data=dat,groups=class.var,
      name.groups=names, rename.level=TRUE, ndim=3,graph=FALSE)
      
summary(res)

Principal component analysis of mixed data

Description

Performs principal component analysis of a set of individuals (observations) described by a mixture of qualitative and quantitative variables. PCAmix includes ordinary principal component analysis (PCA) and multiple correspondence analysis (MCA) as special cases.

Usage

PCAmix(X.quanti = NULL, X.quali = NULL, ndim = 5, rename.level = FALSE,
  weight.col.quanti = NULL, weight.col.quali = NULL, graph = TRUE)

Arguments

Details

If X.quali is not specified (i.e. NULL), only quantitative variables are available and standard PCA is performed. If X.quanti is NULL, only qualitative variables are available and standard MCA is performed.

Missing values are replaced by means for quantitative variables and by zeros in the indicator matrix for qualitative variables.

PCAmix performs squared loadings in (sqload). Squared loadings for a qualitative variable are correlation ratios between the variable and the principal components. For a quantitative variable, squared loadings are the squared correlations between the variable and the principal components.

Note that when all the p variables are qualitative, the factor coordinates (scores) of the n observations are equal to the factor coordinates (scores) of standard MCA times square root of p and the eigenvalues are then equal to the usual eigenvalues of MCA times p. When all the variables are quantitative, PCAmix gives exactly the same results as standard PCA.

Value

Author(s)

Marie Chavent marie.chavent@u-bordeaux.fr, Amaury Labenne.

References

Chavent M., Kuentz-Simonet V., Labenne A., Saracco J., Multivariate analysis of mixed data: The PCAmixdata R package, arXiv:1411.4911 [stat.CO].

See Also

print.PCAmix, summary.PCAmix, predict.PCAmix, plot.PCAmix

Examples

#PCAMIX:
data(wine)
str(wine)
X.quanti <- splitmix(wine)$X.quanti
X.quali <- splitmix(wine)$X.quali
pca<-PCAmix(X.quanti[,1:27],X.quali,ndim=4)
pca<-PCAmix(X.quanti[,1:27],X.quali,ndim=4,graph=FALSE)
pca$eig
pca$ind$coord

#PCA:
data(decathlon)
quali<-decathlon[,13]
pca<-PCAmix(decathlon[,1:10])
pca<-PCAmix(decathlon[,1:10], graph=FALSE)
plot(pca,choice="ind",coloring.ind=quali,cex=0.8,
     posleg="topright",main="Scores")
plot(pca, choice="sqload",main="Squared correlations")
plot(pca, choice="cor",main="Correlation circle")
pca$quanti$coord

#MCA
data(flower)
mca <- PCAmix(X.quali=flower[,1:4],rename.level=TRUE)
mca <- PCAmix(X.quali=flower[,1:4],rename.level=TRUE,graph=FALSE)
plot(mca,choice="ind",main="Scores")
plot(mca,choice="sqload",main="Correlation ratios")
plot(mca,choice="levels",main="Levels")
mca$levels$coord

#Missing values
data(vnf)
PCAmix(X.quali=vnf,rename.level=TRUE)
vnf2<-na.omit(vnf)
PCAmix(X.quali=vnf2,rename.level=TRUE)

Varimax rotation in PCAmix

Description

Orthogonal rotation in PCAmix by maximization of the varimax function expressed in terms of PCAmix squared loadings (correlation ratios for qualitative variables and squared correlations for quantitative variables). PCArot includes the ordinary varimax rotation in Principal Component Analysis (PCA) and a varimax-type rotation in Multiple Correspondence Analysis (MCA) as special cases.

Usage

PCArot(obj, dim, itermax = 100, graph = TRUE)

Arguments

Details

If X.quali is not specified (i.e. NULL) in the previous PCAmix step, only quantitative variables are available and standard varimax rotation in PCA is performed. If X.quanti is NULL, only qualitative variables are available and varimax-type rotation in MCA is performed. Note that p1 is the number of quantitative variables, p2 is the number of qualitative variables and m is the total number of levels of the p2 qualitative variables.

Value

Author(s)

Marie Chavent marie.chavent@u-bordeaux.fr, Vanessa Kuentz, Benoit Liquet, Jerome Saracco

References

Chavent, M., Kuentz, V., Saracco, J. (2011), Orthogonal Rotation in PCAMIX. Advances in Classification and Data Analysis, Vol. 6, pp. 131-146.

Chavent M., Kuentz-Simonet V., Labenne A., Saracco J., Multivariate analysis of mixed data: The PCAmixdata R package, arXiv:1411.4911 [stat.CO].

Kiers, H.A.L., (1991), Simple structure in Component Analysis Techniques for mixtures of qualitative and quantitative variables, Psychometrika, 56, 197-212.

See Also

plot.PCAmix, summary.PCAmix, PCAmix, predict.PCAmix

Examples

#PCAMIX:
data(wine)
pca<-PCAmix(X.quanti=wine[,c(3:29)],X.quali=wine[,1:2],ndim=4,graph=FALSE)
pca

rot<-PCArot(pca,3)
rot
rot$eig #percentages of variances after rotation

plot(rot,choice="ind",coloring.ind=wine[,1],
	    posleg="bottomleft", main="Rotated scores")
plot(rot,choice="sqload",main="Squared loadings after rotation")
plot(rot,choice="levels",main="Levels after rotation")
plot(rot,choice="cor",main="Correlation circle after rotation")



#PCA:
data(decathlon)
quali<-decathlon[,13]
pca<-PCAmix(decathlon[,1:10], graph=FALSE)

rot<-PCArot(pca,3)
plot(rot,choice="ind",coloring.ind=quali,cex=0.8,
	    posleg="topright",main="Scores after rotation")
plot(rot, choice="sqload", main="Squared correlations after rotation")
plot(rot, choice="cor", main="Correlation circle after rotation")

#MCA
data(flower)
mca <- PCAmix(X.quali=flower[,1:4],rename.level=TRUE,graph=FALSE)

rot<-PCArot(mca,2)
plot(rot,choice="ind",main="Scores after rotation")
plot(rot, choice="sqload", main="Correlation ratios after rotation")
plot(rot, choice="levels", main="Levels after rotation")

Performance in decathlon (data)

Description

The data used here refer to athletes' performance during two sporting events.

Usage

data(decathlon)

Format

A data frame with 41 rows and 13 columns: the first ten columns corresponds to the performance of the athletes for the 10 events of the decathlon. The columns 11 and 12 correspond respectively to the rank and the points obtained. The last column is a categorical variable corresponding to the sporting event (2004 Olympic Game or 2004 Decastar)

Source

The references below.

References

Departement of Applied Mathematics, Agrocampus Rennes.

Le, S., Josse, J. & Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software. 25(1). pp. 1-18.

Breeds of Dogs data

Description

Data refering to 27 breeds of dogs.

Format

A data frame with 27 rows (the breeds of dogs) and 7 columns: their size, weight and speed with 3 categories (small, medium, large), their intelligence (low, medium, high), their affectivity and aggressiveness with 3 categories (low, high), their function (utility, compagny, hunting).

Source

Originated by A. Brefort (1982) and cited in Saporta G. (2011).

Flower Characteristics

Description

8 characteristics for 18 popular flowers.

Usage

data(flower)

Format

A data frame with 18 observations on 8 variables:

V1

winters, is binary and indicates whether the plant may be left in the garden when it freezes.

V2

shadow, is binary and shows whether the plant needs to stand in the shadow.

V3

tubers, is asymmetric binary and distinguishes between plants with tubers and plants that grow in any other way.

V4

color, is nominal and specifies the flower's color (1 = white, 2 = yellow, 3 = pink, 4 = red, 5 = blue).

V5

soil, is ordinal and indicates whether the plant grows in dry (1), normal (2), or wet (3) soil.

V6

preference, is ordinal and gives someone's preference ranking going from 1 to 18.

V7

height, is interval scaled, the plant's height in centimeters.

V8

distance, is interval scaled, the distance in centimeters that should be left between the plants.

Source

The reference below.

References

Anja Struyf, Mia Hubert & Peter J. Rousseeuw (1996): Clustering in an Object-Oriented Environment. Journal of Statistical Software, 1. http://www.stat.ucla.edu/journals/jss/

gironde

Description

A list of 4 datasets caracterizing conditions of life of 542 cities in Gironde. The four datasets correspond to four thematics relative to conditions of life. Each dataset contains a different number of variables (quantitative and/or qualitative). The first three datasets come from the 2009 population census realized in Gironde by INSEE (Institut National de la Statistique et des Etudes Economiques). The fourth come from an IGN (Institut National de l'Information Geographique et forestiere) database.

Usage

data(gironde)

Format

A list of 4 data frames.

Value

Source

www.INSEE.fr

www.ign.fr

http://siddt.grenoble.cemagref.fr/

Multivariate analysis of mixed data: The PCAmixdata R package, M. Chavent, V. Kuentz-Simonet, A. Labenne, J. Saracco, arXiv:1411.4911 [stat.CO]

Graphical outputs of MFAmix

Description

Displays the graphical outputs of MFAmix. Individuals (observations), quantitative variables and levels of the qualitative variables are plotted as points using their factor coordinates (scores) in MFAmix. All the variables (quantitative and qualitative) are plotted on the same graph as points using their squared loadings. The groups of variables are plotted using their contributions to the component coordinates. Partial axes and partial individuals of separated analyses can also be plotted.

Usage

## S3 method for class 'MFAmix'
plot(x, axes = c(1, 2), choice = "ind", label = TRUE,
  coloring.var = "not", coloring.ind = NULL, nb.partial.axes = 3,
  col.ind = NULL, col.groups = NULL, partial = NULL, lim.cos2.plot = 0,
  lim.contrib.plot = 0, xlim = NULL, ylim = NULL, cex = 1,
  main = NULL, leg = TRUE, posleg = "topleft", cex.leg = 0.8,
  col.groups.sup = NULL, posleg.sup = "topright", nb.paxes.sup = 3, ...)

Arguments

Details

The observations can be colored according to the levels of a qualitative variable. The observations, the quantitative variables and the levels can be selected according to their squared cosine (lim.cos2.plot) or their relative contribution (lim.contrib.plot) to the component map. Only points with squared cosine or relative contribution greater than a given threshold are plotted. Note that the relative contribution of a point to the component map (a plan) is the sum of the absolute contributions to each dimension, divided by the sum of the corresponding eigenvalues.

Author(s)

, marie.chavent@u-bordeaux.fr, Amaury Labenne

References

Chavent M., Kuentz-Simonet V., Labenne A., Saracco J., Multivariate analysis of mixed data: The PCAmixdata R package, arXiv:1411.4911 [stat.CO].

See Also

summary.PCAmix,PCAmix,PCArot

Examples

data(gironde)
class.var<-c(rep(1,9),rep(2,5),rep(3,9),rep(4,4))
names <- c("employment","housing","services","environment")
dat <- cbind(gironde$employment[1:20,],gironde$housing[1:20,],
           gironde$services[1:20,],gironde$environment[1:20,])
res <- MFAmix(data=dat,groups=class.var,
            name.groups=names, rename.level=TRUE, ndim=3,graph=FALSE)

#---- quantitative variables
plot(res,choice="cor",cex=0.6)
plot(res,choice="cor",cex=0.6,coloring.var="groups")
plot(res,choice="cor",cex=0.6,coloring.var="groups",
     col.groups=c("red","yellow","pink","brown"),leg=TRUE)

#----partial axes
plot(res,choice="axes",cex=0.6)
plot(res,choice="axes",cex=0.6,coloring.var="groups")
plot(res,choice="axes",cex=0.6,coloring.var="groups",
     col.groups=c("red","yellow","pink","brown"),leg=TRUE)

#----groups
plot(res,choice="groups",cex=0.6)   #no colors for groups
plot(res,choice="groups",cex=0.6,coloring.var="groups") 
plot(res,choice="groups",cex=0.6,coloring.var="groups",
     col.groups=c("red","yellow","pink","blue")) 
#----squared loadings
plot(res,choice="sqload",cex=0.8)    #no colors for groups
plot(res,choice="sqload",cex=0.8,coloring.var="groups",
     posleg="topright") 
plot(res,choice="sqload",cex=0.6,coloring.var="groups",
     col.groups=c("red","yellow","pink","blue"),ylim=c(0,1)) 
plot(res,choice="sqload",cex=0.8,coloring.var="type",
     cex.leg=0.9,posleg="topright")  

#----individuals 
plot(res,choice="ind",cex=0.6) 

#----individuals with squared cosine greater than 0.5
plot(res,choice="ind",cex=0.6,lim.cos2.plot=0.5)  

#----individuals colored with a qualitative variable
nbchem <- gironde$services$chemist[1:20]
plot(res,choice="ind",cex=0.6,coloring.ind=nbchem,
     posleg="topright")   
plot(res,choice="ind",coloring.ind=nbchem,
     col.ind=c("pink","brown","darkblue"),label=FALSE,posleg="topright")     

#----partial individuals colored by groups
plot(res,choice="ind",partial=c("AUBIAC","ARCACHON"),
    cex=0.6,posleg="bottomright")

#----levels of qualitative variables
plot(res,choice="levels",cex=0.8)
plot(res,choice="levels",cex=0.8,coloring.var="groups")

#levels with squared cosine greater than 0.6
plot(res,choice="levels",cex=0.8, lim.cos2.plot=0.6)

#supplementary groups
data(wine)
X.quanti <- splitmix(wine)$X.quanti[,1:5]
X.quali <- splitmix(wine)$X.quali[,1,drop=FALSE]
X.quanti.sup <- splitmix(wine)$X.quanti[,28:29]
X.quali.sup <- splitmix(wine)$X.quali[,2,drop=FALSE]
data <- cbind(X.quanti,X.quali)
data.sup <- cbind(X.quanti.sup,X.quali.sup)

groups <-c(1,2,2,3,3,1)
name.groups <- c("G1","G2","G3")
groups.sup <- c(1,1,2)
name.groups.sup <- c("Gsup1","Gsup2")
mfa <- MFAmix(data,groups,name.groups,ndim=4,rename.level=TRUE,graph=FALSE)
mfa.sup <- supvar(mfa,data.sup,groups.sup,name.groups.sup,rename.level=TRUE)
plot(mfa.sup,choice="sqload",coloring.var="groups")
plot(mfa.sup,choice="axes",coloring.var="groups")
plot(mfa.sup,choice="groups",coloring.var="groups")
plot(mfa.sup,choice="levels",coloring.var="groups")
plot(mfa.sup,choice="levels")
plot(mfa.sup,choice="cor",coloring.var = "groups")

Graphical outputs of PCAmix and PCArot

Description

Displays the graphical outputs of PCAmix and PCArot. The individuals (observations), the quantitative variables and the levels of the qualitative variables are plotted as points using their factor coordinates (scores). All the variables (quantitative and qualitative) are plotted as points on the same graph using their squared loadings.

Usage

## S3 method for class 'PCAmix'
plot(x, axes = c(1, 2), choice = "ind", label = TRUE,
  coloring.ind = NULL, col.ind = NULL, coloring.var = FALSE,
  lim.cos2.plot = 0, lim.contrib.plot = 0, posleg = "topleft",
  xlim = NULL, ylim = NULL, cex = 1, leg = TRUE, main = NULL,
  cex.leg = 1, ...)

Arguments

Details

The observations can be colored according to the levels of a qualitative variable. The observations, the quantitative variables and the levels can be selected according to their squared cosine (lim.cos2.plot) or their relative contribution (lim.contrib.plot) to the component map. Only points with squared cosine or relative contribution greater than a given threshold are plotted. Note that the relative contribution of a point to the component map (a plan) is the sum of the absolute contributions to each dimension, divided by the sum of the corresponding eigenvalues.

Author(s)

Marie Chavent marie.chavent@u-bordeaux.fr, Amaury Labenne

References

Chavent M., Kuentz-Simonet V., Labenne A., Saracco J., Multivariate analysis of mixed data: The PCAmixdata R package, arXiv:1411.4911 [stat.CO].

See Also

summary.PCAmix,PCAmix,PCArot

Examples

data(gironde)
base <- gironde$housing[1:20,]
X.quanti <-splitmix(base)$X.quanti
X.quali <- splitmix(base)$X.quali
res<-PCAmix(X.quanti, X.quali, rename.level=TRUE, ndim=3,graph=FALSE)

#----quantitative variables on the correlation circle
plot(res,choice="cor",cex=0.8)

#----individuals component map
plot(res,choice="ind",cex=0.8)

#----individuals colored with the qualitative variable "houses"
houses <- X.quali$houses
plot(res,choice="ind",cex=0.6,coloring.ind=houses) 

#----individuals selected according to their cos2
plot(res,choice="ind",cex=0.6,lim.cos2.plot=0.8)
#----all the variables plotted with the squared loadings
plot(res,choice="sqload",cex=0.8)

#----variables colored according to their type (quanti or quali)
plot(res,choice="sqload",cex=0.8,coloring.var=TRUE) 

#----levels component map
plot(res,choice="levels",cex=0.8)

#----example with supplementary variables
data(wine)
X.quanti <- splitmix(wine)$X.quanti[,1:5]
X.quali <- splitmix(wine)$X.quali[,1,drop=FALSE]
X.quanti.sup <-splitmix(wine)$X.quanti[,28:29]
X.quali.sup <-splitmix(wine)$X.quali[,2,drop=FALSE]
pca<-PCAmix(X.quanti,X.quali,ndim=4,graph=FALSE)
pca2 <- supvar(pca,X.quanti.sup,X.quali.sup)
plot(pca2,choice="levels")
plot(pca2,choice="cor")
plot(pca2,choice="sqload")

Prediction of new scores in MFAmix

Description

This function performs the scores of new observations on the principal components of MFAmix. In other words, this function is projecting the new observations onto the principal components of MFAmix obtained previoulsy on a separated dataset. Note that the new observations must be described with the same variables than those used in MFAmix. The groups of variables must also be identical.

Usage

## S3 method for class 'MFAmix'
predict(object, data, rename.level = FALSE, ...)

Arguments

Value

Returns the matrix of the scores of the new observations on the principal components or on the rotated principal components of MFAmix.

Author(s)

Marie Chavent marie.chavent@u-bordeaux.fr, Amaury Labenne.

References

Chavent M., Kuentz-Simonet V., Labenne A., Saracco J., Multivariate analysis of mixed data: The PCAmixdata R package, arXiv:1411.4911 [stat.CO].

See Also

MFAmix

Examples

data(gironde)
class.var<-c(rep(1,9),rep(2,5),rep(3,9),rep(4,4))
names<-c("employment","housing","services","environment")
dat<-cbind(gironde$employment,gironde$housing,
           gironde$services,gironde$environment)
n <- nrow(dat)
set.seed(10)
sub <- sample(1:n,520)

res<-MFAmix(data=dat[sub,],groups=class.var,
            name.groups=names, rename.level=TRUE, 
            ndim=3,graph=FALSE)

#Predict scores of new data
pred<-predict(res,data=dat[-sub,])
plot(res,choice="ind",cex=0.6,lim.cos2.plot=0.7)  
points(pred[1:5,c(1,2)],col=2,pch=16,cex=0.6)
text(pred[1:5,c(1,2)], labels = rownames(dat[-sub,])[1:5],
     col=2,pos=3,cex=0.6)

Prediction of new scores in PCAmix or PCArot

Description

This function performs the scores of new observations on the principal components of PCAmix. If the components have been rotated, this function performs the scores of the new observations on the rotated principal components. In other words, this function is projecting the new observations onto the principal components of PCAmix (or PCArot) obtained previoulsy on a separated dataset. Note that the new observations must be described with the same variables than those used in PCAmix (or PCArot).

Usage

## S3 method for class 'PCAmix'
predict(object, X.quanti = NULL, X.quali = NULL,
  rename.level = FALSE, ...)

Arguments

Value

Returns the matrix of the scores of the new observations on the principal components or on the rotated principal components of PCAmix.

Author(s)

Marie Chavent marie.chavent@u-bordeaux.fr, Amaury Labenne.

References

Chavent M., Kuentz-Simonet V., Labenne A., Saracco J., Multivariate analysis of mixed data: The PCAmixdata R package, arXiv:1411.4911 [stat.CO].

See Also

PCAmix,PCArot

Examples

# quantitative data
data(decathlon)
n <- nrow(decathlon)
sub <- sample(1:n,20)
pca<-PCAmix(decathlon[sub,1:10], graph=FALSE)
predict(pca,decathlon[-sub,1:10])
rot <- PCArot(pca,dim=4)
predict(rot,decathlon[-sub,1:10])

# quantitative and qualitative data
data(wine)
str(wine)
X.quanti <- splitmix(wine)$X.quanti
X.quali <- splitmix(wine)$X.quali
pca<-PCAmix(X.quanti[,1:27],X.quali,ndim=4,graph=FALSE)
n <- nrow(wine)
sub <- sample(1:n,10)
pca<-PCAmix(X.quanti[sub,1:27],X.quali[sub,],ndim=4)
pred <- predict(pca,X.quanti[-sub,1:27],X.quali[-sub,])
plot(pca,axes=c(1,2))
points(pred[,c(1,2)],col=2,pch=16)
text(pred[,c(1,2)], labels = rownames(X.quanti[-sub,1:27]), col=2,pos=3)

Print a 'MFAmix' object

Description

This is a method for the function print for objects of the class MFAmix.

Usage

  ## S3 method for class 'MFAmix'
print(x, ...)

Arguments

See Also

MFAmix

Print a 'PCAmix' object

Description

This is a method for the function print for objects of the class PCAmix.

Usage

## S3 method for class 'PCAmix'
print(x, ...)

Arguments

See Also

PCAmix , PCArot

Protein data

Description

The data measure the amount of protein consumed for nine food groups in 25 European countries. The nine food groups are red meat (RedMeat), white meat (WhiteMeat), eggs (Eggs), milk (Milk), fish (Fish), cereal (Cereal), starch (Starch), nuts (Nuts), and fruits and vegetables (FruitVeg).

Format

A data frame with 25 rows (the European countries) and 9 columns (the food groups)

Source

Originated by A. Weber and cited in Hand et al., A Handbook of Small Data Sets, (1994, p. 297).

Recoding of the data matrices

Description

Recoding of the quantitative and of the qualitative data matrix.

Usage

  recod(X.quanti, X.quali,rename.level=FALSE)

Arguments

Value

Recoding of the qualitative data matrix.

Description

Recoding of the qualitative data matrix.

Usage

recodqual(X,rename.level=FALSE)

Arguments

Value

Examples

data(vnf)
X <- vnf[1:10,9:12]
tab.disjonctif.NA(X)
recodqual(X)

Recoding of the quantitative data matrix

Description

Recoding of the quantitative data matrix.

Usage

  recodquant(X)

Arguments

Value

Examples

data(decathlon)
X <- decathlon[1:5,1:5]
X[1,2] <- NA
X[2,3] <-NA
rec <- recodquant(X)

splitgroups

Description

If the p variables of a data matrix of dimension (n,p) are separated into G groups, this functions splits this data matrix into G datasets according the groups membership.

Usage

  splitgroups(data, groups, name.groups)

Arguments

Value

Examples

data(decathlon)
split <- splitgroups(decathlon,groups=c(rep(1,10),2,2,3),
          name.groups=c("Epreuve","Classement","Competition"))
split$data.groups$Epreuve

splitmix

Description

Splits a mixed data matrix in two data sets: one with the quantitative variables and one with the qualitative variables. Here, the columns of class "integer are considered quantitative. If you want this column to be considered as qualitative, it must be of class character of factor.

Usage

  splitmix(data)

Arguments

Value

Examples

data(decathlon)
data.split <- splitmix(decathlon)
data.split$X.quanti
data.split$X.quali

Summary of a 'MFAmix' object

Description

This is a method for the function summary for objects of the class MFAmix.

Usage

  ## S3 method for class 'MFAmix'
summary(object, ...)

Arguments

Value

Returns the total number of observations, the number of quantitative variables, the number of qualitative variables with the total number of levels. And all those values are also given by groups.

See Also

plot.MFAmix,MFAmix

Summary of a 'PCAmix' object

Description

This is a method for the function summary for objects of the class PCAmix.

Usage

## S3 method for class 'PCAmix'
summary(object, ...)

Arguments

Value

Returns the matrix of squared loadings. For quantitative variables (resp. qualitative), squared loadings are the squared correlations (resp. the correlation ratios) with the scores or with the rotated (standardized) scores.

See Also

plot.PCAmix,PCAmix,PCArot,

Examples

data(wine)
X.quanti <- wine[,c(3:29)] 
X.quali <- wine[,c(1,2)] 
pca<-PCAmix(X.quanti,X.quali,ndim=4, graph=FALSE)
summary(pca)

rot<-PCArot(pca,3,graph=FALSE)
summary(rot)

Supplementary variables projection

Description

supvar is a generic function for adding supplementary variables in PCAmix or MFAmix. The function invokes invokes two methods which depend on the class of the first argument.

Usage

supvar(obj, ...)

Arguments

Details

This generic function has two methods supvar.PCAmix and supvar.MFAmix

Supplementary variables in MFAmix

Description

Performs the coordinates of supplementary variables and groups on the component of an object of class MFAmix.

Usage

## S3 method for class 'MFAmix'
supvar(obj, data.sup, groups.sup, name.groups.sup,
  rename.level = FALSE, ...)

Arguments

Examples

data(wine)
X.quanti <- splitmix(wine)$X.quanti[,1:5]
X.quali <- splitmix(wine)$X.quali[,1,drop=FALSE]
X.quanti.sup <- splitmix(wine)$X.quanti[,28:29]
X.quali.sup <- splitmix(wine)$X.quali[,2,drop=FALSE]
data <- cbind(X.quanti,X.quali)
data.sup <- cbind(X.quanti.sup,X.quali.sup)
groups <-c(1,2,2,3,3,1)
name.groups <- c("G1","G2","G3")
groups.sup <- c(1,1,2)
name.groups.sup <- c("Gsup1","Gsup2")
mfa <- MFAmix(data,groups,name.groups,ndim=4,rename.level=TRUE,graph=FALSE)
mfa.sup <- supvar(mfa,data.sup,groups.sup,name.groups.sup,rename.level=TRUE)

Supplementary variables in PCAmix

Description

Performs the coordinates of supplementary variables on the component of an object of class PCAmix.

Usage

## S3 method for class 'PCAmix'
supvar(obj, X.quanti.sup = NULL, X.quali.sup = NULL,
  rename.level = FALSE, ...)

Arguments

See Also

PCAmix

Examples

data(wine)
X.quanti <- splitmix(wine)$X.quanti[,1:5]
X.quali <- splitmix(wine)$X.quali[,1,drop=FALSE]
X.quanti.sup <-splitmix(wine)$X.quanti[,28:29]
X.quali.sup <-splitmix(wine)$X.quali[,2,drop=FALSE]
pca<-PCAmix(X.quanti,X.quali,ndim=4,graph=FALSE)
pcasup <- supvar(pca,X.quanti.sup,X.quali.sup)

Built an indicator matrix

Description

This function built the indicator matrix of a qualitative data matrix. Missing observations are indicated as NAs.

Usage

tab.disjonctif.NA(tab, rename.level = FALSE)

Arguments

Details

This function uses the code of the function tab.disjonctif implemented in the package FactoMineR but is different. Here, a NA value appears when a category has not been observed in a row. In the function tab.disjonctif of the package FactoMineR, a new column is created in that case.

Value

Returns the indicator matrix with NA for missing observations.

Examples

data(vnf)
X <- vnf[1:10,9:12]
tab.disjonctif.NA(X)

User satisfaction survey with 1232 individuals and 14 questions

Description

A user satisfaction survey of pleasure craft operators on the “Canal des Deux Mers”, located in South of France, was carried out by the public corporation “Voies Navigables de France” (VNF) responsible for managing and developing the largest network of navigable waterways in Europe

Usage

data(vnf)

Format

A data frame with 1232 observations and 14 qualitative variables.

Source

Josse, J., Chavent, M., Liquet, B. and Husson, F. (2012). Handling missing values with Regularized Iterative Multiple Correspondence Analysis. Journal of classification, Vol. 29, pp. 91-116.

Wine

Description

The data used here refer to 21 wines of Val de Loire.

Usage

data(wine)

Format

A data frame with 21 rows (the number of wines) and 31 columns: the first column corresponds to the label of origin, the second column corresponds to the soil, and the others correspond to sensory descriptors.

Source

Centre de recherche INRA d'Angers

Le, S., Josse, J. & Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software. 25(1). pp. 1-18.