FI_test

Smaller simulated dataset drawn from the same distribution as FI_train and FI_true.
 This dataset is entirely the same as FI_true except this one has 5% of its values missing.
 Used with FastImputation.

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_test function

A data frame with 9 variables and 250 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 Test Data — FI_test

A data frame with 9 variables and 250 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_test: Imputation Test Data

Description

Usage

Arguments

Format

Author