add_missingness

Helper function to add missing values to data.

preprocessing

A tool for multiply imputing missing data using 'MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; <doi:10.1017/pan.2020.49>). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with 'Python' to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>.

Thomas Robinson

rMIDAS

Multiple Imputation with Denoising Autoencoders

Ranjit Lall

Alex Stenlake

Elviss Dvinskis

add_missingness function

<dl><dt>X</dt>
<dd>A data.frame or similar</dd>
<dt>prop</dt>
<dd>Numeric value between 0 and 1; the proportion of observations set to missing</dd>
<dt>cols</dt>
<dd>A vector of column names to be corrupted; if NULL, all columns are used</dd></dl>

Arguments

Apply MAR missingness to data — add_missingness

<dl>

<dt>X</dt>
<dd>A data.frame or similar</dd>


<dt>prop</dt>
<dd>Numeric value between 0 and 1; the proportion of observations set to missing</dd>


<dt>cols</dt>
<dd>A vector of column names to be corrupted; if NULL, all columns are used</dd>

</dl>

add_missingness: Apply MAR missingness to data

Description

Usage

Value

Arguments

Examples