imputationPRV

Calculate the proportional reduction of variance in imputation models.

A collection of R functions that are widely used by the Petersen
Lab. Included are functions for various purposes, including evaluating the
accuracy of judgments and predictions, performing scoring of assessments,
generating correlation matrices, conversion of data between various types,
data management, psychometric evaluation, extensions related to latent
variable modeling, various plotting capabilities, and other miscellaneous
useful functions. By making the package available, we hope to make our
methods reproducible and replicable by others and to help others perform
their data processing and analysis methods more easily and efficiently. The
codebase is provided in Petersen (2024) <doi:10.5281/zenodo.7602890> and on
CRAN: <doi: 10.32614/CRAN.package.petersenlab>. The package is described in
"Principles of Psychological Assessment: With Applied Examples in R"
(Petersen, 2024, 2025) <doi:10.1201/9781003357421>,
<doi:10.25820/work.007199>, <doi:10.5281/zenodo.6466589>.

Isaac T. Petersen

petersenlab

A Collection of R Functions by the Petersen Lab

Developmental Psychopathology Lab at the University of Iowa 

Angela D. Staples

Johanna Caskey

Philipp Doebler

Loreen Sabel

imputationPRV function

<dl><dt>baseline</dt>
<dd>The baseline model object fit with the imputed data.</dd>
<dt>full</dt>
<dd>The full model object fit with the imputed data.</dd>
<dt>baselineTime</dt>
<dd>The position of the random effect of time (random slopes) among the random slopes in the baseline model. For example:<ul>
<li><code>0</code> = no random slopes</li>
<li><code>1</code> = time is the 1st random effect</li>
<li><code>2</code> = time is the second random effect</li>
</ul></dd>
<dt>fullTime</dt>
<dd>The position of the random effect of time (random slopes) among the random slopes in the full model. For example:<ul>
<li><code>0</code> = no random slopes</li>
<li><code>1</code> = time is the 1st random effect</li>
<li><code>2</code> = time is the second random effect</li>
</ul></dd></dl>

Arguments

Proportional Reduction of Variance from Imputation Models. — imputationPRV

<dl>

<dt>baseline</dt>
<dd>The baseline model object fit with the imputed data.</dd>


<dt>full</dt>
<dd>The full model object fit with the imputed data.</dd>


<dt>baselineTime</dt>
<dd>The position of the random effect of time (random slopes) among the random slopes in the baseline model. For example:<ul>
<li><code>0</code> = no random slopes</li>
<li><code>1</code> = time is the 1st random effect</li>
<li><code>2</code> = time is the second random effect</li>
</ul></dd>


<dt>fullTime</dt>
<dd>The position of the random effect of time (random slopes) among the random slopes in the full model. For example:<ul>
<li><code>0</code> = no random slopes</li>
<li><code>1</code> = time is the 1st random effect</li>
<li><code>2</code> = time is the second random effect</li>
</ul></dd>

</dl>

imputationPRV: Proportional Reduction of Variance from Imputation Models.

Description

Usage

Value

Arguments

Details

See Also

Examples