print.gerbil

Prints a <code>gerbil</code> object. Printed output includes a variable-by-variable summary of variable types and missingness rates. The implemented predictor matrix is also provided.

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

print.gerbil function

<dl><dt>x</dt>
<dd>object of <code>gerbil</code> class</dd>
<dt>...</dt>
<dd>additional parameters to be passed down to inner functions.</dd></dl>

Arguments

Prints a gerbil object. Printed output includes a variable-by-variable summary of variable types and missingness rates. The implemented predictor matrix is also provided. — print.gerbil

<dl>

<dt>x</dt>
<dd>object of <code>gerbil</code> class</dd>


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

</dl>

Prints a <code>gerbil</code> object. Printed output includes a variable-by-variable summary of variable types and missingness rates. The implemented predictor matrix is also provided.

print.gerbil: Prints a `gerbil` object. Printed output includes a variable-by-variable summary of variable types and missingness rates. The implemented predictor matrix is also provided.

Description

Usage

Value

Arguments