NIPALS.Biplot <- function(X, alpha = 1, dimension = 3, Scaling = 5, Type="Regular", grouping=NULL, ...) {
# Vamos a probar si esta cosa se actualiza
if (is.data.frame(X))
X = as.matrix(X)
n = nrow(X)
p = ncol(X)
# Setting the properties of data
if (is.null(rownames(X)))
rownames(X) <- rownames(X, do.NULL = FALSE, prefix = "I")
RowNames = rownames(X)
if (is.null(colnames(X)))
colnames(X) <- colnames(X, do.NULL = FALSE, prefix = "V")
VarNames = colnames(X)
DimNames = "Dim 1"
for (i in 2:dimension) DimNames = c(DimNames, paste("Dim", i))
Biplot = list()
if (Type=="Sparse") Biplot$Title = "Sparse NIPALS Biplot"
if (Type=="Truncated") Biplot$Title = "Truncated NIPALS Biplot"
if (Type=="Regular") Biplot$Title = "NIPALS Biplot"
Biplot$Type = "NIPALS"
Biplot$call <- match.call()
Biplot$Non_Scaled_Data = X
Biplot$alpha=alpha
Biplot$Dimension=dimension
Biplot$Means = apply(X, 2, mean)
Biplot$Medians = apply(X, 2, median)
Biplot$Deviations = apply(X, 2, sd)
Biplot$Minima = apply(X, 2, min)
Biplot$Maxima = apply(X, 2, max)
Biplot$P25 = apply(X, 2, quantile)[2, ]
Biplot$P75 = apply(X, 2, quantile)[4, ]
Biplot$GMean = mean(X)
ContinuousDataTransform = c("Raw Data", "Substract the global mean", "Double centering",
"Column centering", "Standardize columns", "Row centering",
"Standardize rows", "Divide by the column means and center",
"Normalized residuals from independence", "Divide by the range",
"Within groups standardization", "Ranks")
if (is.numeric(Scaling))
Scaling = ContinuousDataTransform[Scaling]
Biplot$Initial_Transformation = Scaling
Data = InitialTransform(X, transform = Scaling, grouping=grouping)
X = Data$X
if (Scaling=="Within groups standardization") Biplot$Deviations = Data$ColStdDevs
rownames(X) = RowNames
colnames(X) = VarNames
Biplot$Scaled_Data = X
# Calculating the Biplot
if (Type =="Sparse") SD = Sparse.NIPALSPCA(X, dimens = dimension, ...)
if (Type =="Truncated") SD = Truncated.NIPALSPCA(X, dimens = dimension, ...)
if (Type =="Regular") SD = NIPALSPCA(X, dimens = dimension, ...)
a = SD$u %*% diag(SD$d)
b = SD$v
rownames(a) <- RowNames
colnames(a) <- DimNames
rownames(b) <- VarNames
colnames(b) <- DimNames
CorrXCP = cor(X, a, method = "pearson")
# Relative contributions of the rows and columns
sf = apply((X^2), 1, sum)
cf=matrix(0,n,dimension)
sc = apply((X^2), 2, sum)
cfacum=matrix(0, n, dimension)
ccacum=matrix(0, p, dimension)
cf=matrix(0, n, dimension)
cc=matrix(0, p, dimension)
EV = matrix(0, dimension, 1)
EVacum = matrix(0, dimension, 1)
for (k in 1:dimension){
Fitted.Val= a[,1:k] %*% t(b[,1:k])
EVacum[k]=sum(Fitted.Val^2)/sum(X^2)
cfacum[,k] = apply((Fitted.Val^2), 1, sum)/sf
ccacum[,k] = apply((Fitted.Val^2), 2, sum)/sc
if (k==1){
EV[k]=EVacum[k]
cc[,k]=ccacum[,k]
cf[,k]=cfacum[,k]
}
else{
EV[k]=EVacum[k] - EVacum[k-1]
cc[,k]=ccacum[,k] - ccacum[,(k-1)]
cf[,k]=cfacum[,k] - cfacum[,(k-1)]
}
}
rownames(cfacum) = RowNames
colnames(cfacum) = DimNames
rownames(ccacum) = VarNames
colnames(ccacum) = DimNames
Inertia = round((SD$d^2/sum(X^2)) * 100, digits = 3)
CumInertia = cumsum(Inertia)
if (alpha <= 1) {
sca = sum(a^2)
scb = sum(b^2)
sca = sca/n
scb = scb/p
scf = sqrt(sqrt(scb/sca))
a = a * scf
b = b/scf
} else {
b = b %*% diag(SD$d)
scf = 1
}
Biplot$nrows = n
Biplot$ncols = p
Biplot$dim = dimension
Biplot$EigenValues = SD$d^2
Biplot$Inertia = Inertia
Biplot$CumInertia = CumInertia
Biplot$V = SD$v
Biplot$Structure = CorrXCP
Biplot$RowCoordinates <- a
Biplot$ColCoordinates <- b
# Contributions
Biplot$RowContributions <- cf
Biplot$ColContributions <- cc
Biplot$Scale_Factor = scf
Biplot$ClusterType="us"
Biplot$Clusters = as.factor(matrix(1,nrow(Biplot$RowContributions), 1))
Biplot$ClusterColors="blue"
Biplot$ClusterNames="Cluster 1"
class(Biplot) <- "ContinuousBiplot"
return(Biplot)
}
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