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This function is deprecated because the new version of specr uses a new analytic framework.
In this framework, you can plot a similar figure simply by using the generic plot()
function and adding the argument type = "variance". This functions creates a simple
barplot that visually displays how much variance in the outcome (e.g., the regression coefficient)
different analytical choices or combinations therefor account for. To use this approach,
one needs to estimate a multilevel model that includes all analytical choices as
grouping variables (see examples and vignettes). This function uses icc_specs()
to compute the intraclass correlation coefficients (ICCs), which provides the data
basis for the plot (see examples).'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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    <h1>Plot variance decomposition</h1>
    <small class="dont-index">Source: <a href="https://github.com/masurp/specr/blob/HEAD/R/plot_variance.r" class="external-link"><code>R/plot_variance.r</code></a></small>
    <div class="hidden name"><code>plot_variance.Rd</code></div>
    </div>

    <div class="ref-description">
    <p><a href="https://lifecycle.r-lib.org/articles/stages.html#deprecated" class="external-link"><img src="figures/lifecycle-deprecated.svg" alt="[Deprecated]"></a>
This function is deprecated because the new version of specr uses a new analytic framework.
In this framework, you can plot a similar figure simply by using the generic <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot()</a></code>
function and adding the argument <code>type = "variance"</code>. This functions creates a simple
barplot that visually displays how much variance in the outcome (e.g., the regression coefficient)
different analytical choices or combinations therefor account for. To use this approach,
one needs to estimate a multilevel model that includes all analytical choices as
grouping variables (see examples and vignettes). This function uses <code><a href="icc_specs.html">icc_specs()</a></code>
to compute the intraclass correlation coefficients (ICCs), which provides the data
basis for the plot (see examples).</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">plot_variance</span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></code></pre></div>
    </div>

    <div id="arguments">
    <h2>Arguments</h2>
    <dl><dt>model</dt>
<dd><p>a multilevel model that captures the variances of the specification curve (based on the data frame resulting from <code>run_specs</code>).</p></dd>

</dl></div>
    <div id="value">
    <h2>Value</h2>
    

<p>a <a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a> object.</p>
    </div>
    <div id="see-also">
    <h2>See also</h2>
    <div class="dont-index"><p><code><a href="icc_specs.html">icc_specs()</a></code> to produce a tibble that details the variance decomposition.</p></div>
    </div>

    <div id="ref-examples">
    <h2>Examples</h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Step 1: Run spec curve analysis</span></span></span>
<span class="r-in"><span><span class="va">results</span> <span class="op">&lt;-</span> <span class="fu"><a href="run_specs.html">run_specs</a></span><span class="op">(</span>df <span class="op">=</span> <span class="va">example_data</span>,</span></span>
<span class="r-in"><span>                     y <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"y1"</span>, <span class="st">"y2"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span>                     x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"x1"</span>, <span class="st">"x2"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span>                     model <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"lm"</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Step 2: Estimate multilevel model</span></span></span>
<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/lme4/lme4/" class="external-link">lme4</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/lme4/man/lmer.html" class="external-link">lmer</a></span><span class="op">(</span><span class="va">estimate</span> <span class="op">~</span> <span class="fl">1</span> <span class="op">+</span> <span class="op">(</span><span class="fl">1</span><span class="op">|</span><span class="va">x</span><span class="op">)</span>  <span class="op">+</span> <span class="op">(</span><span class="fl">1</span><span class="op">|</span><span class="va">y</span><span class="op">)</span>, data <span class="op">=</span> <span class="va">results</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Step 3: Plot model</span></span></span>
<span class="r-in"><span><span class="fu">plot_variance</span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>`plot_variance()` was deprecated in specr 1.0.0.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Please use `plot.specr.object()` instead.</span>
<span class="r-plt img"><img src="plot_variance-1.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
</code></pre></div>
    </div>
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