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    <title>topic Re: Excel percent.inc() in SAS Programming</title>
    <link>https://communities.sas.com/t5/SAS-Programming/Excel-percent-inc/m-p/481561#M124592</link>
    <description>&lt;P&gt;Based on the R definition, type one is what excel is doing and type 3 is what SAS is doing. I have tried all of the qtldef and pctldef and was wondering if there was a pre built way to tie to the type 1 definition that I did not see.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Q[i](p) = (1 - γ) x[j] + γ x[j+1],&lt;/P&gt;&lt;P&gt;where 1 ≤ i ≤ 9, (j-m)/n ≤ p &amp;lt; (j-m+1)/n, x[j] is the jth order statistic, n is the sample size, the value of γ is a function of j = floor(np + m) and g = np + m - j, and m is a constant determined by the sample quantile type.&lt;/P&gt;&lt;P&gt;Discontinuous sample quantile types 1, 2, and 3&lt;/P&gt;&lt;P&gt;For types 1, 2 and 3, Q[i](p) is a discontinuous function of p, with m = 0 when i = 1 and i = 2, and m = -1/2 when i = 3.&lt;/P&gt;&lt;P&gt;Type 1&lt;BR /&gt;Inverse of empirical distribution function. γ = 0 if g = 0, and 1 otherwise.&lt;/P&gt;&lt;P&gt;Type 2&lt;BR /&gt;Similar to type 1 but with averaging at discontinuities. γ = 0.5 if g = 0, and 1 otherwise.&lt;/P&gt;&lt;P&gt;Type 3&lt;BR /&gt;SAS definition: nearest even order statistic. γ = 0 if g = 0 and j is even, and 1 otherwise.&lt;/P&gt;</description>
    <pubDate>Thu, 26 Jul 2018 15:42:18 GMT</pubDate>
    <dc:creator>Larrihoover</dc:creator>
    <dc:date>2018-07-26T15:42:18Z</dc:date>
    <item>
      <title>Excel percent.inc()</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Excel-percent-inc/m-p/481544#M124583</link>
      <description>&lt;P&gt;Is there a pre - built quartile function that ties to excels percent.inc() function? I found one that works in R which I can use proc iml to do the trick but was trying to see if there was one in SAS. I can code the logic if needed but my question was more in search of a pre built in way to do this as follows:&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;proc means data= look&amp;nbsp; p75 p90 qtldef=1;&lt;BR /&gt;class periodid;&lt;BR /&gt;var num_lt_660&amp;nbsp;;&lt;/P&gt;&lt;P&gt;output out=look_qrtl (drop = _type_ _freq_ ) p75= p90= /autoname;&lt;BR /&gt;where periodid = &amp;amp;m_date;&lt;BR /&gt;run;&lt;/P&gt;</description>
      <pubDate>Thu, 26 Jul 2018 15:11:08 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Excel-percent-inc/m-p/481544#M124583</guid>
      <dc:creator>Larrihoover</dc:creator>
      <dc:date>2018-07-26T15:11:08Z</dc:date>
    </item>
    <item>
      <title>Re: Excel percent.inc()</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Excel-percent-inc/m-p/481552#M124587</link>
      <description>&lt;P&gt;I'm not really clear on what you're looking for, but perhaps these are close.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I usually use PROC RANK&amp;nbsp; with group = 100 to get the percentiles but it can do various other calculations or groupings.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;PROC UNIVARIATE will also calculate any percentile level.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Thu, 26 Jul 2018 15:17:26 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Excel-percent-inc/m-p/481552#M124587</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2018-07-26T15:17:26Z</dc:date>
    </item>
    <item>
      <title>Re: Excel percent.inc()</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Excel-percent-inc/m-p/481561#M124592</link>
      <description>&lt;P&gt;Based on the R definition, type one is what excel is doing and type 3 is what SAS is doing. I have tried all of the qtldef and pctldef and was wondering if there was a pre built way to tie to the type 1 definition that I did not see.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Q[i](p) = (1 - γ) x[j] + γ x[j+1],&lt;/P&gt;&lt;P&gt;where 1 ≤ i ≤ 9, (j-m)/n ≤ p &amp;lt; (j-m+1)/n, x[j] is the jth order statistic, n is the sample size, the value of γ is a function of j = floor(np + m) and g = np + m - j, and m is a constant determined by the sample quantile type.&lt;/P&gt;&lt;P&gt;Discontinuous sample quantile types 1, 2, and 3&lt;/P&gt;&lt;P&gt;For types 1, 2 and 3, Q[i](p) is a discontinuous function of p, with m = 0 when i = 1 and i = 2, and m = -1/2 when i = 3.&lt;/P&gt;&lt;P&gt;Type 1&lt;BR /&gt;Inverse of empirical distribution function. γ = 0 if g = 0, and 1 otherwise.&lt;/P&gt;&lt;P&gt;Type 2&lt;BR /&gt;Similar to type 1 but with averaging at discontinuities. γ = 0.5 if g = 0, and 1 otherwise.&lt;/P&gt;&lt;P&gt;Type 3&lt;BR /&gt;SAS definition: nearest even order statistic. γ = 0 if g = 0 and j is even, and 1 otherwise.&lt;/P&gt;</description>
      <pubDate>Thu, 26 Jul 2018 15:42:18 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Excel-percent-inc/m-p/481561#M124592</guid>
      <dc:creator>Larrihoover</dc:creator>
      <dc:date>2018-07-26T15:42:18Z</dc:date>
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