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	<title>Curving Normality &#187; R-Project</title>
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	<link>http://www.rensenieuwenhuis.nl</link>
	<description>&#34;The extra-ordinary lies within the curve of normality&#34;</description>
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		<title>Index of the R-Sessions</title>
		<link>http://www.rensenieuwenhuis.nl/index-of-the-r-sessions/</link>
		<comments>http://www.rensenieuwenhuis.nl/index-of-the-r-sessions/#comments</comments>
		<pubDate>Mon, 17 May 2010 10:00:19 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[R]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1209</guid>
		<description><![CDATA[The R-Sessions are a series of blog entries on using R. A large part consists of an R-manual I once wrote. Other posts include some tricks I found out, as well as entries detailing functions and packages I wrote for R. The series already entails over forty posts, so I decided to create an index. [...]]]></description>
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		<slash:comments>2</slash:comments>
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		<title>R Sessions 33: Select (nested) observations with equal number of occurences</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-33-select-nested-observations-with-equal-number-of-occurences/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-33-select-nested-observations-with-equal-number-of-occurences/#comments</comments>
		<pubDate>Wed, 23 Sep 2009 10:00:05 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[balanced data]]></category>
		<category><![CDATA[merge]]></category>
		<category><![CDATA[R]]></category>
		<category><![CDATA[subset]]></category>
		<category><![CDATA[unbalanced data]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1107</guid>
		<description><![CDATA[Recently, I was contacted with an question about R code. A befriended researcher was working with nested data, which was unbalanced. He was working with data in a &#8216;long&#8217; format: all observations nested within the same group had the same identification number. But, the number of observations in each of the groups differed (hence: unbalanced [...]]]></description>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>Influence.ME: Simple Analysis</title>
		<link>http://www.rensenieuwenhuis.nl/influence-me-simple-analysis/</link>
		<comments>http://www.rensenieuwenhuis.nl/influence-me-simple-analysis/#comments</comments>
		<pubDate>Thu, 16 Jul 2009 11:00:19 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[example]]></category>
		<category><![CDATA[influential data]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[mixed effects]]></category>
		<category><![CDATA[multilevel]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1015</guid>
		<description><![CDATA[With the introduction of our new package for influential data influence.ME, I&#8217;m currently writing a manual for the package. This manual will address topics for both the experienced, and the inexperienced users. I will also present much of the content of this manual on my blog. Of course, feel free to comment on it, and [...]]]></description>
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		<slash:comments>0</slash:comments>
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		<title>Presenting influence.ME at useR!</title>
		<link>http://www.rensenieuwenhuis.nl/presenting-influence-me-at-user/</link>
		<comments>http://www.rensenieuwenhuis.nl/presenting-influence-me-at-user/#comments</comments>
		<pubDate>Fri, 10 Jul 2009 09:49:33 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[mixed effects]]></category>
		<category><![CDATA[multilevel]]></category>
		<category><![CDATA[social sciences]]></category>
		<category><![CDATA[useR!]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1028</guid>
		<description><![CDATA[Today I presented influence.ME at the useR! conference in Rennes. Influence.ME is an R package for detecting influential data in mixed models. I developed this package together with Ben Pelzer and Manfred te Grotenhuis. More information about influence.ME can be found on another section of my website. Below, please find the slides of the presentation. [...]]]></description>
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		<slash:comments>2</slash:comments>
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		<title>Influence.ME: don&#8217;t specify the intercept</title>
		<link>http://www.rensenieuwenhuis.nl/influence-me-dont-specify-the-intercept/</link>
		<comments>http://www.rensenieuwenhuis.nl/influence-me-dont-specify-the-intercept/#comments</comments>
		<pubDate>Thu, 18 Jun 2009 11:00:00 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[intercept]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[lmer]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1003</guid>
		<description><![CDATA[Just recently, I was contacted by a researcher who wanted to use influence.ME to obtain model estimates from which iteratively some data was deleted. In his case, observations were nested within an area, but there were very unequal numbers of observations in each area. Unfortunately, he wasn&#8217;t able to use the influence.ME package on his [...]]]></description>
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		<slash:comments>0</slash:comments>
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		<title>One outlier and you&#8217;re out: Influential data and racial prejudice</title>
		<link>http://www.rensenieuwenhuis.nl/one-outlier-and-youre-out-influential-data-and-racial-prejudice/</link>
		<comments>http://www.rensenieuwenhuis.nl/one-outlier-and-youre-out-influential-data-and-racial-prejudice/#comments</comments>
		<pubDate>Tue, 16 Jun 2009 11:00:54 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[IAT]]></category>
		<category><![CDATA[influential data]]></category>
		<category><![CDATA[outlier]]></category>
		<category><![CDATA[prejudice]]></category>
		<category><![CDATA[psychology]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1008</guid>
		<description><![CDATA[Currently preparing a presentation on analyzing influential data in mixed effects models myself, my eye fell on an article in which important claims on racial prejudice were refuted. An important aspect of the criticism on existing work, is that in one article the main correlation was completely due to a single observation. Solely based on [...]]]></description>
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		<slash:comments>4</slash:comments>
		</item>
		<item>
		<title>Introducing Influence.ME: Tools for detecting influential data in mixed models</title>
		<link>http://www.rensenieuwenhuis.nl/introducing-influenceme/</link>
		<comments>http://www.rensenieuwenhuis.nl/introducing-influenceme/#comments</comments>
		<pubDate>Wed, 29 Apr 2009 09:03:25 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[lmer]]></category>
		<category><![CDATA[mixed models]]></category>
		<category><![CDATA[R-Project]]></category>
		<category><![CDATA[regression]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=914</guid>
		<description><![CDATA[I&#8217;m highly excited to announce that influence.ME is now available. Influence.ME is a new software package for R, providing statistical tools for detecting influential data in mixed models. It has been developed by Rense Nieuwenhuis, Ben Pelzer, and Manfred te Grotenhuis. The basic rationale behind identifying influential data is that when iteratively single units are [...]]]></description>
		<wfw:commentRss>http://www.rensenieuwenhuis.nl/introducing-influenceme/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>useR! 2009 acceptance: presenting influence.ME</title>
		<link>http://www.rensenieuwenhuis.nl/user-2009-acceptance-presenting-influenceme/</link>
		<comments>http://www.rensenieuwenhuis.nl/user-2009-acceptance-presenting-influenceme/#comments</comments>
		<pubDate>Thu, 23 Apr 2009 10:23:56 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Influence.ME]]></category>
		<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[glmer]]></category>
		<category><![CDATA[influential data]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[lmer]]></category>
		<category><![CDATA[R]]></category>
		<category><![CDATA[R-Project]]></category>
		<category><![CDATA[useR! 2009]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=935</guid>
		<description><![CDATA[The organizing committee of the useR! 2009 conference just informed me, that my submission for presenting my extension package influence.ME, has been accepted! Influence.ME is a new R package that I&#8217;m currently developing, with the indispensable help of Ben Pelzer and Manfred te Grotenhuis. Although I did not yet introduce influence.ME on this blog, rest [...]]]></description>
		<wfw:commentRss>http://www.rensenieuwenhuis.nl/user-2009-acceptance-presenting-influenceme/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>R-Sessions 32: Forward.lmer: Basic stepwise function for mixed effects in R</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-32/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-32/#comments</comments>
		<pubDate>Fri, 13 Feb 2009 10:59:03 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[forward]]></category>
		<category><![CDATA[hierarchical]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[mixed effects models]]></category>
		<category><![CDATA[multilevel]]></category>
		<category><![CDATA[stepwise]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=897</guid>
		<description><![CDATA[Intended to be a customized solution, it may have grown to be a little more. forward.lmer is an early installment of a full stepwise function for mixed effects regression models in R-Project. I may put in some work to extend it, or I may not. Nevertheless, in a &#8216;forward sense of stepwise&#8217;, I think it [...]]]></description>
		<wfw:commentRss>http://www.rensenieuwenhuis.nl/r-sessions-32/feed/</wfw:commentRss>
		<slash:comments>6</slash:comments>
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		<item>
		<title>R-Sessions 31: Combining lmer output in a single table (UPDATED)</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-31-combining-lmer-output-in-a-single-table/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-31-combining-lmer-output-in-a-single-table/#comments</comments>
		<pubDate>Thu, 05 Feb 2009 11:00:38 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[lmer]]></category>
		<category><![CDATA[mixed effect models]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=891</guid>
		<description><![CDATA[There are various ways of getting your output from R to your publication draft. Most of them are highly efficient, but unfortunately I couldn&#8217;t find a function that combines the output from several (lmer) models and presents it in a single table. lmer is the mixed effects model function from the lme4 package. So, I [...]]]></description>
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		<slash:comments>1</slash:comments>
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