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 = 1e-06, 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 PCA-like 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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