introNAs

Function for testing missing value imputation algorithms

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

introNAs function

<dl><dt>data</dt>
<dd>a dataset without missing values.</dd><dt>percent</dt>
<dd>the percent data that should be randomly assigned as missing</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

Introduce NA's into a Dataframe — introNAs

<dl>

<dt>data</dt>
<dd>a dataset without missing values.</dd>

<dt>percent</dt>
<dd>the percent data that should be randomly assigned as missing</dd>

</dl>

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

introNAs: Introduce NA's into a Dataframe

Description

Usage

Arguments

Author

Details

Examples