plot.clus_lme

Diagnostic plots for the linear mixed-effect model, fitted by clus_lme.

Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for:
(i) true class fractions (TCFs) at fixed pairs of thresholds;
(ii) the ROC surface;
(iii) the volume under ROC surface (VUS);
(iv) the optimal pairs of thresholds.
Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) <doi:10.1177/09622802221089029>. Referring to point (iv), three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) <doi:10.1177/0962280217742539>. Visualization tools are also provided. We refer readers to the articles cited above for all details.

Duc-Khanh To

ClusROC

ROC Analysis in Three-Class Classification Problems for
Clustered Data

Gianfranco Adimari

Monica Chiogna

plot.clus_lme function

<dl><dt>x</dt>
<dd>an object of class "clus_lme", i.e., a result of <code>clus_lme</code> call.</dd>
<dt>file_name</dt>
<dd>File name to create on disk.</dd>
<dt>...</dt>
<dd>further arguments used with <code>ggexport</code> function, for example, <code>width</code>, <code>height</code>.</dd></dl>

Arguments

Plot an clus_lme object. — plot.clus_lme

<dl>

<dt>x</dt>
<dd>an object of class "clus_lme", i.e., a result of <code>clus_lme</code> call.</dd>


<dt>file_name</dt>
<dd>File name to create on disk.</dd>


<dt>...</dt>
<dd>further arguments used with <code>ggexport</code> function, for example, <code>width</code>, <code>height</code>.</dd>

</dl>

plot.clus_lme: Plot an clus_lme object.

Description

Usage

Value

Arguments

Details

See Also