summary.gof_gerbil

Summarises a <code>gerbil</code> object. Printed output pertains to the goodness-of-fit tests that are applied in order to compare the distribution between observed and imputed cases for relevant variables or variable pairs.

Implements a new multiple imputation method that draws
imputations from a latent joint multivariate normal model which
underpins generally structured data. This model is constructed using a
sequence of flexible conditional linear models that enables the
resulting procedure to be efficiently implemented on high dimensional
datasets in practice. See Robbins (2021) <arXiv:2008.02243>.

Michael Robbins

gerbil

Generalized Efficient Regression-Based Imputation with Latent
Processes

Max Griswold

Pedro Nascimento de Lima

summary.gof_gerbil function

<dl><dt>object</dt>
<dd>An object of <code>gof_gerbil</code> class</dd>
<dt>...</dt>
<dd>Additional parameters to be passed down to inner functions.</dd></dl>

Arguments

Summarises a gerbil object. Printed output pertains to the goodness-of-fit tests that are applied in order to compare the distribution between observed and imputed cases for relevant variables or variable pairs. — summary.gof_gerbil

<dl>

<dt>object</dt>
<dd>An object of <code>gof_gerbil</code> class</dd>


<dt>...</dt>
<dd>Additional parameters to be passed down to inner functions.</dd>

</dl>

Summarises a <code>gerbil</code> object. Printed output pertains to the goodness-of-fit tests that are applied in order to compare the distribution between observed and imputed cases for relevant variables or variable pairs.

summary.gof_gerbil: Summarises a `gerbil` object. Printed output pertains to the goodness-of-fit tests that are applied in order to compare the distribution between observed and imputed cases for relevant variables or variable pairs.

Description

Usage

Value

Arguments