valid

object of class inheriting from <code>"pls"</code>, <code>"plsda"</code>, 
    <code>"spls"</code> or <code>"splsda"</code>.

object

arguments to pass to <code><a href="/link/nearZeroVar?package=mixOmics&version=5.0-3" rd-options="" data-mini-rdoc="mixOmics::nearZeroVar">nearZeroVar</a></code>.

The valid function has been supersided by the perf function to evaluate the performance of the PLS, sPLS, PLS-DA and sPLS-DA methods. 
     See ?perf for more details

multivariate

regression

The package provide statistical integrative techniques and
        variants to analyse highly dimensional data sets: regularized
        CCA and sparse PLS to unravel relationships between two
        heterogeneous data sets of size (nxp) and (nxq) 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 CCA 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, Independent Principal Component Analysis and
        multilevel analysis using variance decomposition of the data.

valid: Compute validation criterion for PLS, sPLS, PLS-DA and sPLS-DA

Description

Usage

Arguments

encoding

References

See Also