pcaRes: Class for representing a PCA result
In pcaMethods: A collection of PCA methods

This is a class representation of a PCA result

Creating Objects
new("pcaRes", scores=[the scores], loadings=[the loadings], nPcs=[amount of PCs], R2cum=[cumulative R2], nObs=[amount of observations], nVar=[amount of variables], R2=[R2 for each individual PC], sDev=[stdev for each individual PC], centered=[was data centered], center=[original means], varLimit=[what variance limit was exceeded], method=[method used to calculate PCA], missing=[amount of NAs], completeObs=[estimated complete observations])

Slots

scores: "matrix", the calculated scores
loadings: "matrix", the calculated loadings
R2cum: "numeric", the cumulative R2 values
sDev: "numeric", the individual standard deviations of the score vectors
R2: "numeric", the individual R2 values
cvstat: "numeric", cross-validation statistics
nObs: "numeric", number of observations
nVar: "numeric", number of variables
centered: "logical", data was centered or not
center: "numeric", the original variable centers
scaled: "logical", data was scaled or not
scl: "numeric", the original variable scales
varLimit: "numeric", the exceeded variance limit
nPcs,nP: "numeric", the number of calculated PCs
method: "character", the method used to perform PCA
missing: "numeric", the total amount of missing values in original data
completeObs: "matrix", the estimated complete observations
network: "nlpcaNet", the network used by non-linear PCA

Methods (not necessarily exhaustive)

print: Print function
summary: Extract information about PC relevance
screeplot: Plot a barplot of standard deviations for PCs
slplot: Make a side by side score and loadings plot
nPcs: Get the number of PCs
nObs: Get the number of observations
cvstat: Cross-validation statistics
nVar: Get the number of variables
loadings: Get the loadings
scores: Get the scores
dim: Get the dimensions (number of observations, number of features)
centered: Get a logical indicating if centering was done as part of the model
center: Get the averages of the original variables.
completeObs: Get the imputed data set
method: Get a string naming the used PCA method
sDev: Get the standard deviations of the PCs
scaled: Get a logical indicating if scaling was done as part of the model
scl: Get the scales of the original variablesb
R2cum: Get the cumulative R2