imputeRough

print.roughImputation

After generating a cell means model matrix, impute expected values (mean or median for continous; hightest frequency for categorical).

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

imputeRough function

<dl><dt>data</dt>
<dd>a dataset with missing values</dd><dt>Init</dt>
<dd>For continous variables impute either the mean or median</dd></dl>

Arguments

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

Naive Imputation of Missing Values for Dummy Variable Model Matrix — imputeRough

<dl>

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

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

</dl>

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

imputeRough: Naive Imputation of Missing Values for Dummy Variable Model Matrix

Description

Usage

Value

Arguments

Author

Details

Examples