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<table width="100%" summary="page for plot.iio.class"><tr><td>plot.iio.class</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>
Plot iio.class objects
</h2>

<h3>Description</h3>

<p>S3 Method to <code>plot</code> objects of class iio.class.
Graphic display of the checks of iio. 
One graph for each item plotting the estimated item response functions.
</p>


<h3>Usage</h3>

<pre>
## S3 method for class 'iio.class'
plot(x, item.pairs = all.pairs, ci = TRUE, alpha = .05, 
       color = c("black", "blue"), transparancy = 20, ask = TRUE, ...)
</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>x</code></td>
<td>
<p>Object of class iio.class produced by <code>check.iio</code>.</p>
</td></tr>
<tr valign="top"><td><code>item.pairs</code></td>
<td>
<p>vector containing the numbers of the item pairs for which the results are depicted graphically.
For example, <code>item.pairs = 1</code> prints the results for items 1 and 2,
<code>item.pairs = 2</code> prints the results for items 1 and 3,
<code>item.pairs = J</code> prints the results for items 1 and $J$, and
<code>item.pairs = J+1</code> prints the results for items 2 and 3.
Default the results for all item pairs are depicted.</p>
</td></tr>
<tr valign="top"><td><code>ci</code></td>
<td>
<p>Boolean. If <code>TRUE</code> (default), then confidence envelops are plotted around IRFs.</p>
</td></tr>              
<tr valign="top"><td><code>alpha</code></td>
<td>
<p>Type of plotted (1 - alpha) confidence intervals. By default 95-percent confidence intervals are depicted</p>
</td></tr>
<tr valign="top"><td><code>color</code></td>
<td>
<p>Color of the plotted curves and confidence envelops. Defaults are black for the first item and blue for the second item.</p>
</td></tr>
<tr valign="top"><td><code>transparancy</code></td>
<td>
<p>Transparancy of the confidence intervals. Higher values result in more opaque colors for the confidence intervals.</p>
</td></tr> 
<tr valign="top"><td><code>ask</code></td>
<td>
<p>Boolean. If <code>TRUE</code> (default), then <code>par("ask"=TRUE)</code>; i.e., a hard return between subsequent plots is required.
If <code>FALSE</code>, then <code>par("ask"=FALSE)</code>.</p>
</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
<p>Optional graphical parameters will be ignored</p>
</td></tr>  
</table>


<h3>Details</h3>

<p>The plot function corresponds to method MIIO; each graph plots the estimated item response functions (item rest-score functions) for two items. 
For details of the method, see Ligtvoet et al. (2010, 2011); Sijtsma et al. (2012).
For details of the confidence envelopes, see Van der Ark (2012b).
For the implementation in R, see Van der Ark (2012a).
For <code>ask==FALSE</code>, the default graphic device in R may only display the last graph.
</p>


<h3>Value</h3>

<p>Returns a graph.
</p>


<h3>Author(s)</h3>

<p>L. A. van der Ark <a href="mailto:L.A.vanderArk@uva.nl">L.A.vanderArk@uva.nl</a>
</p>


<h3>References</h3>

<p>Koopman, L., Zijlstra, B. J. H., &amp; Van der Ark, L. A. (2023a). 
Assumptions and Properties of Two-Level Nonparametric Item Response 
Theory Models. Manuscript submitted for publication.
</p>
<p>Koopman, L., Zijlstra, B. J. H., &amp; Van der Ark, L. A. (2023b). 
Evaluating Model Fit in Two-Level Mokken Scale Analysis. 
Manuscript submitted for publication.
</p>
<p>Ligtvoet, R., L. A. van der Ark, J. M. te Marvelde, &amp; K. Sijtsma (2010).
Investigating an invariant item ordering for polytomously scored items. 
<em>Educational and Psychological Measurement, 70</em>, 578-595.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1177/0013164409355697")}
</p>
<p>Ligtvoet, R., L. A. van der Ark, W. P. Bergsma, &amp; K. Sijtsma (2011).
Polytomous latent scales for the investigation of the ordering of items.
<em>Psychometrika, 76</em>, 200-216.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1007/s11336-010-9199-8")}
</p>
<p>Sijtsma, K., R. R. Meijer, &amp; Van der Ark, L. A. (2011).
Mokken scale analysis as time goes by: An update for scaling practitioners.
<em>Personality and Individual Differences, 50</em>, 31-37.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1016/j.paid.2010.08.016")} 
</p>
<p>Van der Ark, L. A. (2012). 
New developjements in Mokken scale analysis in <span class="pkg">R</span>. 
<em>Journal of Statistical Software, 48</em> (5), 1-27.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.18637/jss.v048.i05")}
</p>
<p>Van der Ark, L. A. (2014).
Visualizing uncertainty of estimated response functions in nonparametric item response theory.
In R. E. Millsap, L. A. van der Ark, D. Bolt, &amp; C. M. Woods (Eds.),
<em>New developments in quantitative psychology</em> (pp. 59-68). New York: Springer.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1007/978-1-4614-9348-8_5")}
</p>


<h3>See Also</h3>

<p><code>check.iio</code>, <code>summary.iio.class</code>
</p>


<h3>Examples</h3>

<pre>
data(acl)
Communality &lt;- acl[,1:10]
iio.list &lt;- check.iio(Communality)
summary(iio.list)
plot(iio.list)

# Compute two-level fit statistics (Koopman et al., 2023a, 2023b)
data("autonomySupport")
dat &lt;- autonomySupport[, -1]
groups &lt;- autonomySupport[, 1]
autonomyMIIO &lt;- check.iio(dat, item.selection = FALSE, level.two.var = groups)
summary(autonomyMIIO)
plot(autonomyMIIO)


</pre>


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