coalesce_one_hot

Helper function to reverse one-hot encoding post-imputation.

postprocessing

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

coalesce_one_hot function

<dl><dt>X</dt>
<dd>A data.frame, data.table or matrix, for a single variable</dd>
<dt>var_name</dt>
<dd>A character string, with the original variable label</dd>
<dt>fast</dt>
<dd>Boolean, indicating whether to choose category with highest predicted probability (TRUE), or use predicted probabilities as weights in draw from random distribution</dd></dl>

Arguments

Coalesce one-hot encoding back to a single variable — coalesce_one_hot

<dl>

<dt>X</dt>
<dd>A data.frame, data.table or matrix, for a single variable</dd>


<dt>var_name</dt>
<dd>A character string, with the original variable label</dd>


<dt>fast</dt>
<dd>Boolean, indicating whether to choose category with highest predicted probability (TRUE), or use predicted probabilities as weights in draw from random distribution</dd>

</dl>

coalesce_one_hot: Coalesce one-hot encoding back to a single variable

Description

Usage

Value

Arguments