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	<title>Curving Normality &#187; lme4</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>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 ...]]></description>
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		<slash:comments>0</slash:comments>
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		<item>
		<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 ...]]></description>
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		<slash:comments>2</slash:comments>
		</item>
		<item>
		<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 ...]]></description>
		<wfw:commentRss>http://www.rensenieuwenhuis.nl/influence-me-dont-specify-the-intercept/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 ...]]></description>
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		<slash:comments>0</slash:comments>
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		<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 ...]]></description>
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		<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 ...]]></description>
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		<slash:comments>1</slash:comments>
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		<item>
		<title>R-Sessions 19: Extractor Functions</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-19-extractor-functions/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-19-extractor-functions/#comments</comments>
		<pubDate>Fri, 05 Sep 2008 10:58:43 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[anova]]></category>
		<category><![CDATA[extractor functions]]></category>
		<category><![CDATA[fixef]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[multilevel]]></category>
		<category><![CDATA[ranef]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=571</guid>
		<description><![CDATA[<a href="http://www.rensenieuwenhuis.nl/archive/category/r-project/r-sessions/"><img src="http://www.rensenieuwenhuis.nl/wp-content/uploads/2008/07/r-sessions.jpg" " title="R-Sessions" width="470" /></a>

Unlike most statistical software packages, R often stores the results of an analysis in an object. The advantage of this is that while not all output is shown in the screen ad once, it is neither necessary to estimate the statistical model again if different output is required.

This paragraph will show the kind of data that is stored in a multilevel model estimated by R-Project and introduce some functions that make use of this data.]]></description>
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		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>R-Sessions 17: Generalized Multilevel {lme4}</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-17-generalized-multilevel-lme4/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-17-generalized-multilevel-lme4/#comments</comments>
		<pubDate>Mon, 01 Sep 2008 10:00:40 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[generalised]]></category>
		<category><![CDATA[lme4]]></category>
		<category><![CDATA[logistic regression]]></category>
		<category><![CDATA[multilevel]]></category>
		<category><![CDATA[regression]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=552</guid>
		<description><![CDATA[Although all introductions on regression seem to be based on the assumption of data that is distributed normally, in practice this is not the case. Many other types of distributions exist. To name a few: normal distribution, binomial distribution, poisson, gaussian and so on. The lmer()-function in the lme4-package can easily estimate models based on these distributions. This is done by adding the 'family'-argument to the command syntax, thereby specifying that not a linear multilevel model needs to be estimated, but a generalized linear model.]]></description>
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		<slash:comments>1</slash:comments>
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		<item>
		<title>R-Sessions 16: Multilevel Model Specification (lme4)</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-16-multilevel-model-specification-lme4/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-16-multilevel-model-specification-lme4/#comments</comments>
		<pubDate>Wed, 27 Aug 2008 10:00:47 +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 model]]></category>
		<category><![CDATA[multilevel]]></category>
		<category><![CDATA[multilevel regression]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=543</guid>
		<description><![CDATA[<a href="http://www.rensenieuwenhuis.nl/archive/category/r-project/r-sessions/"><img src="http://www.rensenieuwenhuis.nl/wp-content/uploads/2008/07/r-sessions.jpg" " title="R-Sessions" width="470" /></a>
Multilevel models, or mixed effects models, can easily be estimated in R. Several packages are available. Here, the lmer() function from the lme4-package is described. The specification of several types of models will be shown, using a fictive example.  A detailed description of the specification rules is given. Output of the specified models is given, but not described or interpreted. 
Please note that this description is very closely related to the description of the <a href="http://www.rensenieuwenhuis.nl/r-project/manual/multilevel-analysis/model-specification-nlme/">specification of the lme() function of the nlme-package</a>. The results are similar and here exactly the same possibilities are offered.]]></description>
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