Principal Component Analysis (PCA) using R

scree plot of PCA

PCA means Principal Component Analysis. PCA is a multivariate technique that is used to reduce the dimension of a data set. More precisely, PCA is concerned with explaining the variance-covariance structure through a few linear combinations of the original variables. Thus PCA transforms the original set of variables into a smaller set of linear combinations … Read more

Independent Component Analysis (ICA) using R

ICA

ICA means Independent Component Analysis. ICA is the most powerful and widely used statistical technique which is used to separate independent sources from their mixture. It is also known as blind source separation technique. The Situation where we use ICA We can easily simplify the situation where we use ICA by the following example, Imagine … Read more

Canonical Correlation Analysis (CCA) using R

CCA

Canonical correlation analysis (CCA) determines a set of canonical variates, orthogonal linear combinations of the variables within each set that best explain the variability both within and between sets. It is used to identify and measure the associations among two sets of variables. How to perform canonical correlation analysis in R? Follow this step to find canonical … Read more

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