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    <h1>Fitting linear mixed effect models - serverside function</h1>
    
    <div class="hidden name"><code>lmerSLMADS2.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>lmerSLMADS2 is a serverside function which fits a linear mixed
effects model (lme) - i.e. can include both fixed and random effects - on data from
one or multiple sources with pooling via SLMA (study level meta-analysis)</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">lmerSLMADS2</span><span class="op">(</span>
  <span class="va">formula</span>,
  <span class="va">offset</span>,
  <span class="va">weights</span>,
  <span class="va">dataName</span>,
  REML <span class="op">=</span> <span class="cn">TRUE</span>,
  <span class="va">control_type</span>,
  <span class="va">control_value.transmit</span>,
  <span class="va">optimizer</span>,
  verbose <span class="op">=</span> <span class="fl">0</span>
<span class="op">)</span></code></pre></div>
    </div>

    <div id="arguments">
    <h2>Arguments</h2>
    <dl><dt>formula</dt>
<dd><p>see help for ds.lmerSLMA</p></dd>


<dt>offset</dt>
<dd><p>see help for ds.lmerSLMA</p></dd>


<dt>weights</dt>
<dd><p>see help for ds.lmerSLMA</p></dd>


<dt>dataName</dt>
<dd><p>see help for ds.lmerSLMA</p></dd>


<dt>REML</dt>
<dd><p>see help for ds.lmerSLMA</p></dd>


<dt>control_type</dt>
<dd><p>see help for ds.lmerSLMA</p></dd>


<dt>control_value.transmit</dt>
<dd><p>see help for argument &lt;control_value&gt; for
function ds.lmerSLMA</p></dd>


<dt>optimizer</dt>
<dd><p>see help for ds.lmerSLMA</p></dd>


<dt>verbose</dt>
<dd><p>see help for ds.lmerSLMA</p></dd>

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

<p>all key model components see help for ds.lmerSLMA</p>
    </div>
    <div id="details">
    <h2>Details</h2>
    <p>lmerSLMADS2 is a serverside function called by ds.lmerSLMA on the clientside.
The analytic work engine is the lmer function in R which sits in the lme4 package.
ds.lmerSLMA fits a linear mixed effects model (lme) - can include both fixed and random
effects - on data from a single or multiple sources. When there are multiple data sources,
the lme is fitted to convergence in each data source independently and the
estimates and standard errors returned to the client thereby enabling cross-study pooling
using study level meta-analysis (SLMA). By default the SLMA is undertaken
using the metafor package, but as the SLMA occurs on the clientside which, as far
as the user is concerned is just a standard R environment, the user can choose to use
any approach to meta-analysis they choose. For more detailed help about any aspect
of lmerSLMDS2 please see the extensive help for ds.lmerSLMA. Additional information
about fitting lmes using the lmer engine can be obtained using R help for lmer and
the lme4 package</p>
    </div>
    <div id="author">
    <h2>Author</h2>
    <p>Tom Bishop, with some additions by Paul Burton</p>
    </div>

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