PCAOS  R Documentation 
Perform PCAOS
PCAOS( data, level.scale = rep("num", ncol(data)), nb.comp = 2, maxiter = 100, threshold = 1e06, D = 1, supp.var = NULL, print = TRUE, init = "rdm" )
data 
a data frame with n rows (individuals) and p columns (numeric, nominal and/or ordinal variables) 
level.scale 
vector(length p) giving the nature of each variable. Possible values: "nom", "ord", "num". The order of categories for an ordinal variable is indicated by it's level. 
nb.comp 
number of components of the model (by default 2) 
maxiter 
maximum number of iterations 
threshold 
threshold for assessing convergence 
D 
degree of the relation between quantified variables and components (only for numeric variables, default = 1) 
supp.var 
a vector indicating the indexes of the supplementary variables 
print 
boolean (TRUE by default), if TRUE convergence information and order of the categories of ordinal variables are printed. 
init 
Intitialization strategy, possible values are :

Dimension reduction
weigths : list of weights of the variables (loadings and weights are the same in PCAlike model)
components : data.frame with individuals scores for each dimension
inertia : percentage and cumulative percentage of variance of the quantified variables explained
Quantifications
quantified.data : optimally quantified variables
quantification.categories.nom : list of optimally quantified categories (nominal variables)
quantification.categories.ord : list of optimally quantified categories (ordinal variables)
level.scale : nature of scaling choosen for each variable
data : orginal dataset
Algorithm
summary : summary of the number of variables according to their nature
loss.tot : global loss for all variables
stockiter : evolution of the criterion for each ieration
Supplementary variables
var.supp : original supplementary variables
level.scale.supp : level of scaling of supplementary variables
coord.supp.num : coordinates of supplementary numeric variables (correlation with components)
coord.supp.quali : coordinates of qualitatve variables (barycenters)
Martin PARIES (Maintainer: martin.paries@onirisnantes.fr)
Evelyne Vigneau
Stephanie Bougeard
Paries, Bougeard, Vigneau (2022), Multivariate analysis of JustAboutRight data with optimal scaling approach. Food Quality and Preference (submit)
data (antibiotic) # Level of scaling of each variable # Manually level.scale < rep(NA,ncol(antibiotic)) #Setting level.scale argument level.scale[c(3,4)] < "num" level.scale[c(6:14)] < "nom" level.scale[c(1,15)] < "ord" # Or using nature.variables() level.scale < rep(NA,ncol(antibiotic)) res.nature < nature.variables(antibiotic) level.scale [res.nature$p.numeric] < "num" level.scale [res.nature$p.quali] < "nom" #Warning; the ordinal nature of variables can not be detected automaticaly. level.scale[c(1,15)] < "ord" # PCAOS res.PCAOS < PCAOS( data = antibiotic, level.scale = level.scale, nb.comp = 2) # Plot (individuals) plot.PCAOS( x = res.PCAOS, choice = "ind", coloring.indiv = antibiotic$Atb.conso, size.legend = 12, size.label = 4 )
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