v1.4 [Mar 5, 2008]
PRINCIPAL COMPONENT ANALYSIS. Additional outputs for the component: Scree plot and variance explained cumulative curve; PCA Correlation Matrix - Some outputs are provided for the detection of the significant factors (Kaiser-Guttman, Karlis-Saporta-Spinaki, Legendre-Legendre broken-stick test); PCA Correlation Matrix - Bartlett's sphericity test is performed and the Kaiser's measure of sampling adequacy (MSA) is calculated; PCA Correlation Matrix - The correlation matrix and the partial correlations between each pair of variables controlling for all other variables (the negative anti-image correlation) are produced.
PARALLEL ANALYSIS. The component calculates the distribution of eigenvalues for a set of randomly generated data. It proceeds by randomization. It applies to the principal components analysis and te multiple correspondence analysis. A factor is considered significant if its observed eigenvalue is greater than the 95-th percentile (this setting can be modified).
BOOTSTRAP EIGENVALUES. It calculates by bootstrap approach the confidence intervals of eigenvalues. A factor is considered significant if its eigenvalue is greater than a threshold which depends on the underlying factor method (PCA or MCA) method, or if the lower bound of the eigenvalue of a factor is greater than higher bound of the following one. The confidence level 0.90 can be modified. This component can be applied to the principal component analysis or the multiple correspondence analysis.
JITTERING. Jittering feature is incorporated to the scatter plot components (SCATTERPLOT, CORRELATION SCATTERPLOT, SCATTERPLOT WITH LABEL, VIEW MULTIPLE SCATTERPLOT).
RANDOM FOREST. The not used memory is released after the decision tree learning process. This feature is especially useful when we use an ensemble learning approach where we store a large number of trees in memory (BAGGING, BOOSTING, RANDOM FOREST). The memory occupation is reduced. The computation efficiency is improved.
