SeqimputeEM

print.seqem

Missing values are sequentially updated via an EM algorithm.

An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users).   Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.

Nelson Afanador

mvdalab

Multivariate Data Analysis Laboratory

SeqimputeEM function

<dl><dt>data</dt>
<dd>a dataset with missing values.</dd><dt>max.ncomps</dt>
<dd>integer corresponding to the maximum number of components to test</dd><dt>max.ssq</dt>
<dd>maximal SSQ for final number of components. This will be improved by automation.</dd><dt>Init</dt>
<dd>For continous variables impute either the mean or median.</dd><dt>adjmean</dt>
<dd>Adjust (recalculate) mean after each iteration.</dd><dt>max.iters</dt>
<dd>maximum number of iterations for the algorithm.</dd><dt>tol</dt>
<dd>the threshold for assessing convergence.</dd></dl>

Arguments

Thanh Tran (<a href="/link/thanh.tran%40mvdalab.com?package=mvdalab&version=1.7" data-mini-rdoc="mvdalab::thanh.tran@mvdalab.com">thanh.tran@mvdalab.com</a>), Nelson Lee Afanador (<a href="/link/nelson.afanador%40mvdalab.com?package=mvdalab&version=1.7" data-mini-rdoc="mvdalab::nelson.afanador@mvdalab.com">nelson.afanador@mvdalab.com</a>)

Author

Sequential Expectation Maximization (EM) for imputation of missing values. — SeqimputeEM

<dl>

<dt>data</dt>
<dd>a dataset with missing values.</dd>

<dt>max.ncomps</dt>
<dd>integer corresponding to the maximum number of components to test</dd>

<dt>max.ssq</dt>
<dd>maximal SSQ for final number of components. This will be improved by automation.</dd>

<dt>Init</dt>
<dd>For continous variables impute either the mean or median.</dd>

<dt>adjmean</dt>
<dd>Adjust (recalculate) mean after each iteration.</dd>

<dt>max.iters</dt>
<dd>maximum number of iterations for the algorithm.</dd>

<dt>tol</dt>
<dd>the threshold for assessing convergence.</dd>

</dl>

Thanh Tran (<a href='mailto:thanh.tran@mvdalab.com'>thanh.tran@mvdalab.com</a>), Nelson Lee Afanador (<a href='mailto:nelson.afanador@mvdalab.com'>nelson.afanador@mvdalab.com</a>)

SeqimputeEM: Sequential Expectation Maximization (EM) for imputation of missing values.

Description

Usage

Value

Arguments

Author

Details

References

Examples