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<h1>Merge a <code><a href='summary_factorlist.html'>summary_factorlist</a>()</code> table with any number of model
results tables.</h1>
<small class="dont-index">Source: <a href='https://github.com/ewenharrison/finalfit/blob/master/R/ff_merge.R'><code>R/ff_merge.R</code></a></small>
<div class="hidden name"><code>ff_merge.Rd</code></div>
</div>
<div class="ref-description">
<p>A function that takes the output from <code><a href='summary_factorlist.html'>summary_factorlist</a>(...,
fit_id=TRUE)</code> and merges with any number of model dataframes, usually
produced with a model wrapper followed by the <code><a href='fit2df.html'>fit2df</a>()</code> function
(see examples).</p>
</div>
<pre class="usage"><span class='fu'>ff_merge</span><span class='op'>(</span>
<span class='va'>factorlist</span>,
<span class='va'>fit2df_df</span>,
ref_symbol <span class='op'>=</span> <span class='st'>"-"</span>,
estimate_name <span class='op'>=</span> <span class='cn'>NULL</span>,
last_merge <span class='op'>=</span> <span class='cn'>FALSE</span>
<span class='op'>)</span>
<span class='fu'>finalfit_merge</span><span class='op'>(</span>
<span class='va'>factorlist</span>,
<span class='va'>fit2df_df</span>,
ref_symbol <span class='op'>=</span> <span class='st'>"-"</span>,
estimate_name <span class='op'>=</span> <span class='cn'>NULL</span>,
last_merge <span class='op'>=</span> <span class='cn'>FALSE</span>
<span class='op'>)</span></pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>factorlist</th>
<td><p>Output from <code><a href='summary_factorlist.html'>summary_factorlist</a>(...,
fit_id=TRUE)</code>.</p></td>
</tr>
<tr>
<th>fit2df_df</th>
<td><p>Output from model wrappers followed by
<code><a href='fit2df.html'>fit2df</a>()</code>.</p></td>
</tr>
<tr>
<th>ref_symbol</th>
<td><p>Reference symbol for model reference levels, typically "-"
or "1.0".</p></td>
</tr>
<tr>
<th>estimate_name</th>
<td><p>If you have chosen a new `estimate name` (e.g. "Odds
ratio") when running a model wrapper (e.g. `glmuni`), then you need to pass
this new name to `finalfit_merge` to generate correct table. Defaults to
OR/HR/Coefficient</p></td>
</tr>
<tr>
<th>last_merge</th>
<td><p>Logical. Set to try for the final merge in a series to
remove index and fit_id columns.</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>Returns a dataframe of combined tables.</p>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p><code><a href='summary_factorlist.html'>summary_factorlist</a></code> <code><a href='fit2df.html'>fit2df</a></code></p></div>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://github.com/ewenharrison/finalfit'>finalfit</a></span><span class='op'>)</span>
<span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://dplyr.tidyverse.org'>dplyr</a></span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>colon_s</span><span class='op'>)</span>
<span class='va'>explanatory</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"age.factor"</span>, <span class='st'>"sex.factor"</span>, <span class='st'>"obstruct.factor"</span>, <span class='st'>"perfor.factor"</span><span class='op'>)</span>
<span class='va'>explanatory_multi</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"age.factor"</span>, <span class='st'>"obstruct.factor"</span><span class='op'>)</span>
<span class='va'>random_effect</span> <span class='op'>=</span> <span class='st'>"hospital"</span>
<span class='va'>dependent</span> <span class='op'>=</span> <span class='st'>"mort_5yr"</span>
<span class='co'># Create separate tables</span>
<span class='va'>colon_s</span> <span class='op'>%>%</span>
<span class='fu'><a href='summary_factorlist.html'>summary_factorlist</a></span><span class='op'>(</span><span class='va'>dependent</span>, <span class='va'>explanatory</span>, fit_id<span class='op'>=</span><span class='cn'>TRUE</span><span class='op'>)</span> <span class='op'>-></span> <span class='va'>example.summary</span>
</div><div class='output co'>#> <span class='message'>Note: dependent includes missing data. These are dropped.</span></div><div class='input'>
<span class='va'>colon_s</span> <span class='op'>%>%</span>
<span class='fu'><a href='glmuni.html'>glmuni</a></span><span class='op'>(</span><span class='va'>dependent</span>, <span class='va'>explanatory</span><span class='op'>)</span> <span class='op'>%>%</span>
<span class='fu'><a href='fit2df.html'>fit2df</a></span><span class='op'>(</span>estimate_suffix<span class='op'>=</span><span class='st'>" (univariable)"</span><span class='op'>)</span> <span class='op'>-></span> <span class='va'>example.univariable</span>
</div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='input'>
<span class='va'>colon_s</span> <span class='op'>%>%</span>
<span class='fu'><a href='glmmulti.html'>glmmulti</a></span><span class='op'>(</span><span class='va'>dependent</span>, <span class='va'>explanatory</span><span class='op'>)</span> <span class='op'>%>%</span>
<span class='fu'><a href='fit2df.html'>fit2df</a></span><span class='op'>(</span>estimate_suffix<span class='op'>=</span><span class='st'>" (multivariable)"</span><span class='op'>)</span> <span class='op'>-></span> <span class='va'>example.multivariable</span>
</div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='input'>
<span class='va'>colon_s</span> <span class='op'>%>%</span>
<span class='fu'><a href='glmmixed.html'>glmmixed</a></span><span class='op'>(</span><span class='va'>dependent</span>, <span class='va'>explanatory</span>, <span class='va'>random_effect</span><span class='op'>)</span> <span class='op'>%>%</span>
<span class='fu'><a href='fit2df.html'>fit2df</a></span><span class='op'>(</span>estimate_suffix<span class='op'>=</span><span class='st'>" (multilevel)"</span><span class='op'>)</span> <span class='op'>-></span> <span class='va'>example.multilevel</span>
<span class='co'># Pipe together</span>
<span class='va'>example.summary</span> <span class='op'>%>%</span>
<span class='fu'>ff_merge</span><span class='op'>(</span><span class='va'>example.univariable</span><span class='op'>)</span> <span class='op'>%>%</span>
<span class='fu'>ff_merge</span><span class='op'>(</span><span class='va'>example.multivariable</span><span class='op'>)</span> <span class='op'>%>%</span>
<span class='fu'>ff_merge</span><span class='op'>(</span><span class='va'>example.multilevel</span>, last_merge <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
</div><div class='output co'>#> label levels Alive Died OR (univariable)
#> 1 Age <40 years 31 (6.1) 36 (8.9) -
#> 2 40-59 years 208 (40.7) 131 (32.4) 0.54 (0.32-0.92, p=0.023)
#> 3 60+ years 272 (53.2) 237 (58.7) 0.75 (0.45-1.25, p=0.270)
#> 8 Sex Female 243 (47.6) 194 (48.0) -
#> 9 Male 268 (52.4) 210 (52.0) 0.98 (0.76-1.27, p=0.889)
#> 4 Obstruction No 408 (82.1) 312 (78.6) -
#> 5 Yes 89 (17.9) 85 (21.4) 1.25 (0.90-1.74, p=0.189)
#> 6 Perforation No 497 (97.3) 391 (96.8) -
#> 7 Yes 14 (2.7) 13 (3.2) 1.18 (0.54-2.55, p=0.672)
#> OR (multivariable) OR (multilevel)
#> 1 - -
#> 2 0.57 (0.34-0.98, p=0.041) 0.75 (0.39-1.44, p=0.382)
#> 3 0.81 (0.48-1.36, p=0.426) 1.03 (0.55-1.96, p=0.916)
#> 8 - -
#> 9 0.98 (0.75-1.28, p=0.902) 0.80 (0.58-1.11, p=0.180)
#> 4 - -
#> 5 1.25 (0.90-1.76, p=0.186) 1.23 (0.82-1.83, p=0.320)
#> 6 - -
#> 7 1.12 (0.51-2.44, p=0.770) 1.03 (0.43-2.51, p=0.940)</div><div class='input'>
<span class='co'># Using finalfit()</span>
<span class='va'>colon_s</span> <span class='op'>%>%</span>
<span class='fu'><a href='finalfit.html'>finalfit</a></span><span class='op'>(</span><span class='va'>dependent</span>, <span class='va'>explanatory</span>, keep_fit_id <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='op'>%>%</span>
<span class='fu'>ff_merge</span><span class='op'>(</span><span class='va'>example.multilevel</span>, last_merge <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
</div><div class='output co'>#> <span class='message'>Note: dependent includes missing data. These are dropped.</span></div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='output co'>#> <span class='message'>Waiting for profiling to be done...</span></div><div class='output co'>#> Dependent: Mortality 5 year Alive Died
#> 1 Age <40 years 31 (46.3) 36 (53.7)
#> 2 40-59 years 208 (61.4) 131 (38.6)
#> 3 60+ years 272 (53.4) 237 (46.6)
#> 8 Sex Female 243 (55.6) 194 (44.4)
#> 9 Male 268 (56.1) 210 (43.9)
#> 4 Obstruction No 408 (56.7) 312 (43.3)
#> 5 Yes 89 (51.1) 85 (48.9)
#> 6 Perforation No 497 (56.0) 391 (44.0)
#> 7 Yes 14 (51.9) 13 (48.1)
#> OR (univariable) OR (multivariable) OR (multilevel)
#> 1 - - -
#> 2 0.54 (0.32-0.92, p=0.023) 0.57 (0.34-0.98, p=0.041) 0.75 (0.39-1.44, p=0.382)
#> 3 0.75 (0.45-1.25, p=0.270) 0.81 (0.48-1.36, p=0.426) 1.03 (0.55-1.96, p=0.916)
#> 8 - - -
#> 9 0.98 (0.76-1.27, p=0.889) 0.98 (0.75-1.28, p=0.902) 0.80 (0.58-1.11, p=0.180)
#> 4 - - -
#> 5 1.25 (0.90-1.74, p=0.189) 1.25 (0.90-1.76, p=0.186) 1.23 (0.82-1.83, p=0.320)
#> 6 - - -
#> 7 1.18 (0.54-2.55, p=0.672) 1.12 (0.51-2.44, p=0.770) 1.03 (0.43-2.51, p=0.940)</div></pre>
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