FI_train

Larger simulated dataset drawn from the same distribution as FI_test and FI_true and used
 to train the imputation algorithm. 5% of the values are missing. Used with TrainFastImputation.

datasets

TrainFastImputation() uses training data to describe a
multivariate normal distribution that the data approximates or
can be transformed into approximating and stores this information
as an object of class 'FastImputationPatterns'. FastImputation()
function uses this 'FastImputationPatterns' object to impute (make
a good guess at) missing data in a single line or a whole data frame
of data. This approximates the process used by 'Amelia'
<https://gking.harvard.edu/amelia> but is much faster when
filling in values for a single line of data.

Stephen Haptonstahl

FastImputation

Learn from Training Data then Quickly Fill in Missing Data

FI_train function

A data frame with 9 variables and 10000 observations.<dl>
 <dt><code>user_id_1</code></dt>
<dd>Sequential user ids</dd> <dt><code>bounded_below_2</code></dt>
<dd>Multivariate normal, transformed using <code>exp(x)</code></dd> <dt><code>unbounded_3</code></dt>
<dd>Multivariate normal</dd> <dt><code>unbounded_4</code></dt>
<dd>Multivariate normal</dd> <dt><code>bounded_above_5</code></dt>
<dd>Multivariate normal, transformed using <code>-exp(x)</code></dd> <dt><code>bounded_above_and_below_6</code></dt>
<dd>Multivariate normal, transformed using <code>pnorm(x)</code></dd> <dt><code>unbounded_7</code></dt>
<dd>Multivariate normal</dd> <dt><code>unbounded_8</code></dt>
<dd>Multivariate normal</dd> <dt><code>categorical_9</code></dt>
<dd>"A" if the first of three multivariate normal draws is greatest; "B" if the
 second is greatest; "C" if the third is greatest</dd> 
</dl>

Format

Stephen R. Haptonstahl <a href="/link/srh%40haptonstahl.org?package=FastImputation&version=2.2.1" data-mini-rdoc="FastImputation::srh@haptonstahl.org">srh@haptonstahl.org</a>

Author

Imputation Training Data — FI_train

A data frame with 9 variables and 10000 observations.<dl>
 <dt><code>user_id_1</code></dt>
<dd>Sequential user ids</dd>

 <dt><code>bounded_below_2</code></dt>
<dd>Multivariate normal, transformed using <code>exp(x)</code></dd>

 <dt><code>unbounded_3</code></dt>
<dd>Multivariate normal</dd>

 <dt><code>unbounded_4</code></dt>
<dd>Multivariate normal</dd>

 <dt><code>bounded_above_5</code></dt>
<dd>Multivariate normal, transformed using <code>-exp(x)</code></dd>

 <dt><code>bounded_above_and_below_6</code></dt>
<dd>Multivariate normal, transformed using <code>pnorm(x)</code></dd>

 <dt><code>unbounded_7</code></dt>
<dd>Multivariate normal</dd>

 <dt><code>unbounded_8</code></dt>
<dd>Multivariate normal</dd>

 <dt><code>categorical_9</code></dt>
<dd>"A" if the first of three multivariate normal draws is greatest; "B" if the
 second is greatest; "C" if the third is greatest</dd>

 
</dl>

Stephen R. Haptonstahl <a href='mailto:srh@haptonstahl.org'>srh@haptonstahl.org</a>

FI_train: Imputation Training Data

Description

Usage

Arguments

Format

Author