data.xllim.test

<p><code>data.xllim.test</code> is a matrix of simulated testing data, generated under the same GLLiM model as <code>data.xllim</code>, from the true parameters available in object <code>data.xllim.trueparameters</code>. The goal is to train a GLLiM (resp. SLLiM and BLLiM) model on training data (see <code>data.xllim</code>) and to retrieve the unknown responses from data.xllim.test using  <code>gllim_inverse_map</code> (resp. <code>sllim_inverse_map</code>).</p>

Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).

Emeline Perthame

xLLiM

High Dimensional Locally-Linear Mapping

data.xllim.test function

<p>A matrix of simulated testing data with 50 rows (covariates) and 20 columns (observations).</p>

data.xllim.test: Testing data to run examples of usage of `gllim_inverse_map` and `sllim_inverse_map` functions

Description

Usage

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See Also

Examples