forest.mp.cv.val

forest.perf

Draw a forest plot of the performance of an internally-externally cross-validated model.

Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.

Thomas Debray

metamisc

Meta-Analysis of Diagnosis and Prognosis Research Studies

Valentijn de Jong

forest.mp.cv.val function

<dl><dt>object</dt>
<dd>An <code>mp.cv.val</code> or <code>perf</code> object.</dd>
<dt>perfFUN</dt>
<dd>Numeric or character. Which performance statistic should be plotted? Defaults to the first.</dd>
<dt>method</dt>
<dd>character string specifying whether a fixed- or a random-effects model should be used to summarize the
prediction model performance. A fixed-effects model is fitted when using method="FE". Random-effects models are fitted 
by setting method equal to one of the following: "DL", "HE", "SJ", "ML", "REML", "EB", "HS", or "GENQ". Default is "REML".</dd>
<dt>xlab</dt>
<dd>Label on x-axis. Defaults to the name of the performance function.</dd>
<dt>...</dt>
<dd>Other arguments passed to plotting internals. E.g. <code>title</code>. See forest.default for details.</dd></dl>

Arguments

Valentijn de Jong &lt;Valentijn.M.T.de.Jong@gmail.com&gt;

Author

Forest plot of a validation object. — forest.mp.cv.val

<dl>

<dt>object</dt>
<dd>An <code>mp.cv.val</code> or <code>perf</code> object.</dd>


<dt>perfFUN</dt>
<dd>Numeric or character. Which performance statistic should be plotted? Defaults to the first.</dd>


<dt>method</dt>
<dd>character string specifying whether a fixed- or a random-effects model should be used to summarize the
prediction model performance. A fixed-effects model is fitted when using method="FE". Random-effects models are fitted 
by setting method equal to one of the following: "DL", "HE", "SJ", "ML", "REML", "EB", "HS", or "GENQ". Default is "REML".</dd>


<dt>xlab</dt>
<dd>Label on x-axis. Defaults to the name of the performance function.</dd>


<dt>...</dt>
<dd>Other arguments passed to plotting internals. E.g. <code>title</code>. See forest.default for details.</dd>

</dl>

forest.mp.cv.val: Forest plot of a validation object.

Description

Usage

Arguments

Author