<< Statistics
fact
(X) One matrix of contingency >>
fact
>> (X) One matrix of observations/variables
(X) One matrix of observations/variables
cspcana
—
principal component analysis on centered-standardized data
ica_blocs_signals
—
the number of independent components is determined from the correlations between blocs issued from the dataset
ica_dwresiduals
—
the number of independent components in ICA is determined from the Durbin-Watson criterium
icajade
—
yields a matrix for extracting the independent components using the Jade algorithm
icascores
—
extracts independent components (signals, loadings) and proportions (scores) using the icajade function
kcmeans
—
classifies the observations into groups according to the k-means classification
outlier
—
calculation of 3 parameters: T2-Hotelling, Q or residual variances, and leverage, useful to identify outliers
pcaapply
—
applies a PCA model, calculated with a first dataset, to a second dataset
pcana
—
principal component analysis
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<< Statistics
fact
(X) One matrix of contingency >>