pcatune

This function has been renamed <code><a href="/link/tune.pca?package=mixOmics&version=5.2.0" rd-options="" data-mini-rdoc="mixOmics::tune.pca">tune.pca</a></code>.

We provide statistical integrative techniques and
        variants to analyse highly dimensional data sets: regularized
        Canonical Correlation Analysis ('rCCA') and sparse Partial Least Squares variants ('sPLS') 
        to unravel relationships between two heterogeneous data sets of size (n times p) and (n times q) where the p and
        q variables are measured on the same samples or individuals n. These data may come from high throughput 
        technologies, such as 'omics' data (e.g. transcriptomics, metabolomics or proteomics
        data) that require an integrative or joint analysis. However, 'mixOmics' can also be applied to any other 
        large data sets where p + q >> n. 'rCCA' is a regularized version of Canonical Correlation Analysis to deal with
        the large number of variables. 'sPLS' allows variable selection in a one step procedure and two frameworks 
        are proposed: regression and canonical analysis. Numerous graphical outputs are provided to help interpreting 
        the results. Recent methodological developments include: sparse PLS-Discriminant Analysis ('sPLS-DA'), Independent 
        Principal Component Analysis ('IPCA'), multilevel analysis using variance decomposition of the data and integration 
        of multiple data sets with regularized Generalised Canonical Correlation Analysis ('rGCCA') and variants (sparse 'GCCA'). More details  can be found 
        on our website.

pcatune: Tune the number of principal components in PCA

Description

Arguments

encoding