MBPCAOS  R Documentation 
Perform MBPCAOS
MBPCAOS( data, level.scale = rep("num", ncol(data)), blocks, blocks.name = paste("Bloc", 1:length(blocks)), block.scaling = "inertia", nb.comp = 2, maxiter = 100, threshold = 1e06, 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" 
blocks 
vector(length k) with number of variables in each bloc 
blocks.name 
vector(length k) with names of each bloc 
block.scaling 
scaling applied to each block. Possible value are :

nb.comp 
number of components of the model (by default 2) 
maxiter 
maximum number of iterations. 
threshold 
the threshold for assessing convergence 
supp.var 
a vector indicating the indexes of the supplementary variables 
print 
boolean (TRUE by default), if TRUE ther order of the categories of ordinal variables are print 
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
Blocks results
block.components : components associated with each block
block.weight : weights associated with each block
blocks : number of variable in each block
blocks.name : name of each block
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)
data('antibiotic') antb.uses < antibiotic[,c('Atb.conso','Atb.Sys')] health < antibiotic[,c('Age','Loss')] vet.practices < antibiotic[,c(6:15)] antibiotic < data.frame(antb.uses,health,vet.practices) # Defining blocks blocks.name = c("antibiotic.uses","Health.of.turkeys","Veterinary.practices") blocks < c(2,2,10) # Level of scaling 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(1,14)] < "ord" # MBPCAOS res.MBPCAOS < MBPCAOS(data = antibiotic, level.scale = level.scale, blocks = blocks, blocks.name = blocks.name, nb.comp = 3) # Blocks graphs plot.MBPCAOS(x = res.MBPCAOS,choice = 'blocks')
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