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This is the central function of the package and represent the second step
in the analytic framework implemented in the package specr. It estimates
and returns respective parameters and estimates of models that were specified
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    <h1>Fit models across all specifications</h1>
    <small class="dont-index">Source: <a href="https://github.com/masurp/specr/blob/HEAD/R/specr.R" class="external-link"><code>R/specr.R</code></a></small>
    <div class="hidden name"><code>specr.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Runs the specification/multiverse analysis across specified models.
This is the central function of the package and represent the second step
in the analytic framework implemented in the package <code>specr</code>. It estimates
and returns respective parameters and estimates of models that were specified
via <code><a href="setup.html">setup()</a></code>.</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">specr</span><span class="op">(</span><span class="va">x</span>, data <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
    </div>

    <div id="arguments">
    <h2>Arguments</h2>
    <dl><dt>x</dt>
<dd><p>A <code>specr.setup</code> object resulting from <code>setup</code> or a tibble that
contains the relevant specifications (e.g., a tibble resulting from
<code>as_tibble(setup(...))</code>).</p></dd>


<dt>data</dt>
<dd><p>If x is not an object of "specr.setup" and simply a tibble, you
need to provide the data set that should be used. Defaults to NULL as it is
assumend that most users will create an object of class "specr.setup" that they'll
pass to <code>specr()</code>.</p></dd>


<dt>...</dt>
<dd><p>Further arguments that can be passed to <code>future_pmap</code>. This only becomes
important if parallelization is used. For example, if a custom model function is used
this involves passing <code>furrr_options</code> passing to the argument <code>.options</code>.
When a plan for parallelization is set, one can also set <code>.progress = TRUE</code>
to print a progress bar during the fitting process. See details for more information
on parallelization.</p></dd>

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

<p>An object of class <code>specr.object</code>, which includes a data frame
with all specifications their respective results along with many other useful
information about the model. Parameters are extracted via the function passed
to <code>setup</code>. By default this is <code><a href="https://generics.r-lib.org/reference/tidy.html" class="external-link">broom::tidy()</a></code> and the function
<code><a href="https://generics.r-lib.org/reference/glance.html" class="external-link">broom::glance()</a></code>).Several other aspects and information are included in
the resulting class (e.g., number of specifications, time elapsed, subsets
included in the analyses). Use <code>methods(class = "specr.object")</code> for
an overview on available methods.</p>
    </div>
    <div id="details">
    <h2>Details</h2>
    <p>Empirical results are often contingent on analytical decisions that
are equally defensible, often arbitrary, and motivated by different reasons.
This decisions may introduce bias or at least variability. To this end,
specification curve analyses  (Simonsohn et al., 2020) or multiverse
analyses (Steegen et al., 2016) refer to identifying the set of
theoretically justified, statistically valid (and potentially also non-redundant
specifications, fitting the "multiverse" of models represented by these
specifications and extract relevant parameters often to display the results
graphically as a so-called specification curve. This allows readers to
identify consequential specifications decisions and how they affect the results
or parameter of interest.</p>
<p><b>Use of this function</b></p>
<p>A general overview is provided in the vignettes <code>vignette("specr")</code>.
Generally, you create relevant specification using the function <code><a href="setup.html">setup()</a></code>.
You then pass the resulting object of a class <code>specr.setup</code> to the
present function <code>specr()</code> to run the specification curve analysis.
Further note that the resulting object of class <code>specr.object</code> allows
to use several generic function such as <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code> or <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot()</a></code>.
Use <code>methods(class = "specr.object")</code> for an overview on available
methods and e.g., <code><a href="plot.specr.object.html">?plot.specr.object</a></code> to view the dedicated help page.</p>
<p><b>Parallelization</b></p>
<p>By default, the function fits models across all specifications sequentially
(one after the other). If the data set is large, the models complex (e.g.,
large structural equation models, negative binomial models, or Bayesian models),
and the number of specifications is large, it can make sense to parallelize
these operations. One simply has to load the package <code>furrr</code> (which
in turn, builds on <code>future</code>) up front. Then parallelizing the fitting process
works as specified in the package description of <code>furr</code>/<code>future</code> by setting a
"plan" before running <code>specr</code> such as:</p>
<p><code>plan(multisession, workers = 4)</code></p>
<p>However, there are many more ways to specifically set up the plan, including
different strategy than <code>multisession</code>. For more information, see
<code><a href="../articles/parallelization.html">vignette("parallelization")</a></code> and the
<a href="https://future.futureverse.org/reference/plan.html" class="external-link">reference page</a>
for <code>plan()</code>.</p>
<p><b>Disclaimer</b></p>
<p>We do see a lot of value in investigating how analytical choices
affect a statistical outcome of interest. However, we strongly caution
against using specr as a tool to somehow arrive at a better estimate
compared to a single model. Running a specification curve analysis
does not make your findings any more reliable, valid or generalizable
than a single analysis. The method is meant to inform about the effects
of analytical choices on results, and not a better way to estimate a
correlation or effect.</p>
    </div>
    <div id="references">
    <h2>References</h2>
    
<ul><li><p>Simonsohn, U., Simmons, J.P. &amp; Nelson, L.D. (2020). Specification curve analysis. <em>Nature Human Behaviour, 4</em>, 1208–1214. https://doi.org/10.1038/s41562-020-0912-z</p></li>
<li><p>Steegen, S., Tuerlinckx, F., Gelman, A., &amp; Vanpaemel, W. (2016). Increasing Transparency Through a Multiverse Analysis. <em>Perspectives on Psychological Science, 11</em>(5), 702-712. https://doi.org/10.1177/1745691616658637</p></li>
</ul></div>
    <div id="see-also">
    <h2>See also</h2>
    <div class="dont-index"><p><code><a href="setup.html">setup()</a></code> for the first step of setting up the specifications.</p>
<p><code><a href="summary.specr.object.html">summary.specr.object()</a></code> for how to summarize and inspect the results.</p>
<p><code><a href="plot.specr.object.html">plot.specr.object()</a></code> for plotting results.</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"># Example 1 ----</span></span></span>
<span class="r-in"><span><span class="co"># Setup up typical specifications</span></span></span>
<span class="r-in"><span><span class="va">specs</span> <span class="op">&lt;-</span> <span class="fu"><a href="setup.html">setup</a></span><span class="op">(</span>data <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="st">"lm"</span>,</span></span>
<span class="r-in"><span>   controls <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">"c1"</span>, <span class="st">"c2"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span>   subsets <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>group1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/unique.html" class="external-link">unique</a></span><span class="op">(</span><span class="va">example_data</span><span class="op">$</span><span class="va">group1</span><span class="op">)</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"># Run analysis (not parallelized)</span></span></span>
<span class="r-in"><span><span class="va">results</span> <span class="op">&lt;-</span> <span class="fu">specr</span><span class="op">(</span><span class="va">specs</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Summary of the results</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">results</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Results of the specification curve analysis</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> -------------------</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Technical details:</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   Class:                          specr.object -- version: 1.0.1 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   Cores used:                     1 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   Duration of fitting process:    0.5 sec elapsed </span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   Number of specifications:       64 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Descriptive summary of the specification curve:</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span>  median  mad   min  max   q25  q75</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>    0.13 0.41 -0.39 0.66 -0.14 0.39</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Descriptive summary of sample sizes: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span>  median min  max</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   338.5 323 1000</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Head of the specification results (first 6 rows): </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 6 × 25</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   x     y     model controls subsets group1 formula estimate std.error statistic</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;glue&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> x1    y1    lm    no cova… middle  middle y1 ~ x…     0.61      0.07      9.28</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> x1    y1    lm    no cova… old     old    y1 ~ x…     0.66      0.06     10.4 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> x1    y1    lm    no cova… young   young  y1 ~ x…     0.57      0.06     10.2 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> x1    y1    lm    no cova… all     <span style="color: #BB0000;">NA</span>     y1 ~ x…     0.62      0.04     16.4 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> x1    y1    lm    c1       middle  middle y1 ~ x…     0.6       0.07      8.81</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">6</span> x1    y1    lm    c1       old     old    y1 ~ x…     0.64      0.06      9.81</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 15 more variables: p.value &lt;dbl&gt;, conf.low &lt;dbl&gt;, conf.high &lt;dbl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   fit_r.squared &lt;dbl&gt;, fit_adj.r.squared &lt;dbl&gt;, fit_sigma &lt;dbl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   fit_statistic &lt;dbl&gt;, fit_p.value &lt;dbl&gt;, fit_df &lt;dbl&gt;, fit_logLik &lt;dbl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   fit_AIC &lt;dbl&gt;, fit_BIC &lt;dbl&gt;, fit_deviance &lt;dbl&gt;, fit_df.residual &lt;dbl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   fit_nobs &lt;dbl&gt;</span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Example 2 ----</span></span></span>
<span class="r-in"><span><span class="co"># Working without S3 classes</span></span></span>
<span class="r-in"><span><span class="va">specs2</span> <span class="op">&lt;-</span> <span class="fu"><a href="setup.html">setup</a></span><span class="op">(</span>data <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="st">"lm"</span>,</span></span>
<span class="r-in"><span>    controls <span class="op">=</span> <span class="st">"c1"</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Working with tibbles</span></span></span>
<span class="r-in"><span><span class="va">specs_tibble</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://tibble.tidyverse.org/reference/as_tibble.html" class="external-link">as_tibble</a></span><span class="op">(</span><span class="va">specs2</span><span class="op">)</span>      <span class="co"># extract tibble from setup</span></span></span>
<span class="r-in"><span><span class="va">results2</span> <span class="op">&lt;-</span> <span class="fu">specr</span><span class="op">(</span><span class="va">specs_tibble</span>,</span></span>
<span class="r-in"><span>                  data <span class="op">=</span> <span class="va">example_data</span><span class="op">)</span> <span class="co"># need to provide data!</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Results (tibble instead of S3 class)</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">results2</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 6 × 26</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   x     y     model controls      subsets formula  model_function term  estimate</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;glue&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;list&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> x1    y1    lm    no covariates all     y1 ~ x1… <span style="color: #949494;">&lt;fn&gt;</span>           x1       0.620</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> x1    y1    lm    c1            all     y1 ~ x1… <span style="color: #949494;">&lt;fn&gt;</span>           x1       0.603</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> x1    y2    lm    no covariates all     y2 ~ x1… <span style="color: #949494;">&lt;fn&gt;</span>           x1      -<span style="color: #BB0000;">0.328</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> x1    y2    lm    c1            all     y2 ~ x1… <span style="color: #949494;">&lt;fn&gt;</span>           x1      -<span style="color: #BB0000;">0.339</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> x2    y1    lm    no covariates all     y1 ~ x2… <span style="color: #949494;">&lt;fn&gt;</span>           x2       0.272</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">6</span> x2    y1    lm    c1            all     y1 ~ x2… <span style="color: #949494;">&lt;fn&gt;</span>           x2       0.249</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 17 more variables: std.error &lt;dbl&gt;, statistic &lt;dbl&gt;, p.value &lt;dbl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   conf.low &lt;dbl&gt;, conf.high &lt;dbl&gt;, fit_r.squared &lt;dbl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   fit_adj.r.squared &lt;dbl&gt;, fit_sigma &lt;dbl&gt;, fit_statistic &lt;dbl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   fit_p.value &lt;dbl&gt;, fit_df &lt;dbl&gt;, fit_logLik &lt;dbl&gt;, fit_AIC &lt;dbl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   fit_BIC &lt;dbl&gt;, fit_deviance &lt;dbl&gt;, fit_df.residual &lt;int&gt;, fit_nobs &lt;int&gt;</span></span>
</code></pre></div>
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