print.cor_gerbil

Prints a <code>cor_gerbil</code> object. Printed output includes the average difference of correlations, as well as summaries of the test statistics based on Fisher's z and their p-values.

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.cor_gerbil function

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

Arguments

Prints a cor_gerbil object. Printed output includes the average difference of correlations, as well as summaries of the test statistics based on Fisher's z and their p-values. — print.cor_gerbil

<dl>

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


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

</dl>

Prints a <code>cor_gerbil</code> object. Printed output includes the average difference of correlations, as well as summaries of the test statistics based on Fisher's z and their p-values.

print.cor_gerbil: Prints a `cor_gerbil` object. Printed output includes the average difference of correlations, as well as summaries of the test statistics based on Fisher's z and their p-values.

Description

Usage

Value

Arguments