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    <div class="page-header">
    <h1>Secure ranking of "V2BR" (vector to be ranked) across all sources
and use of these ranks to estimate global quantiles across all studies</h1>
    
    <div class="hidden name"><code>extractQuantilesDS2.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>identify the global values of V2BR (i.e. the values across all
studies) that relate to a set of quantiles to be evaluated.</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">extractQuantilesDS2</span><span class="op">(</span><span class="va">extract.summary.output.ranks.df</span><span class="op">)</span></code></pre></div>
    </div>

    <div id="arguments">
    <h2>Arguments</h2>
    <dl><dt>extract.summary.output.ranks.df</dt>
<dd><p>character string specifies an optional
name for the data.frame written to the serverside on each data source that
contains 5 of the key output variables from the ranking procedure pertaining
to that particular data source. This data frame represents the key source of
information - including global ranks - that determines the values of V2BR
that are identified as corresponding to the particular set of quantiles to
be estimated as specified by the &lt;quantiles.for.estimation&gt; argument of
function ds.ranksSecure (and the &lt;extract.quantiles&gt; argument of
ds.extractQuantiles).</p></dd>

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

<p>the single value of V2BR which best corresponds to each key quantile
value to be estimated as specified by the argument &lt;quantiles.for.estimation&gt;
A data frame (final.quantile.df)summarising the results of this analysis
is written to the clientside.  This data frame consists of two vectors. The
first is named "evaluation.quantiles". It lists the full set of quantiles
you have requested for evaluation as specified by the argument
"quantiles.for.estimation" The second vector which is called
"final.quantile.vector" details the values of V2BR that correspond to the
the key quantiles listed in vector 1.</p>
    </div>
    <div id="details">
    <h2>Details</h2>
    <p>Severside aggregate function called by ds.extractQuantiles via
ds.ranksSecure. This takes the "global.bounds.df" data frame saved on the
serverside following construction by extractQuantilesDS1. This
data frame includes the two quantile values that most closely span each
quartile value to be estimated. If either of the values had been the correct 
value for a given quantile, both the bounding values would have taken that
value in global.bounds.df. This is because the upper bound was defined as
the lowest value that was equal to or greater than the true value for that
quantile while the lower bound was defined as the highest value that was
equal to or lower than the true value. Next, the function extractQuantileDS2
goes round study by study to identify the values of V2BR that actually
correspond to each of the spanning values around each quantile. Then the
function goes quantile by quantile and estimates the mean of the
two values of V2BR that correspond to the the spanning quantiles. If these
two values are the same it means that that value of V2BR is the
"true" value and the mean of two (or potentially several) instances of that
value is inevitably also equal to that true value. If the upper and lower
bounding values of V2BR differ, neither can  be the precisely correct single
value of V2BR for that quantile (see above for explanation) and so the mean
of the two is a reasonable interpolated summary.</p>
    </div>
    <div id="author">
    <h2>Author</h2>
    <p>Paul Burton 11th November, 2021</p>
    </div>

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