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	<title>Curving Normality &#187; regression</title>
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	<description>&#34;The extra-ordinary lies within the curve of normality&#34;</description>
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		<title>Super Crunchers &#8211; Ayres (2007) &#8211; 1/2</title>
		<link>http://www.rensenieuwenhuis.nl/super-crunchers-ayres-2007-1/</link>
		<comments>http://www.rensenieuwenhuis.nl/super-crunchers-ayres-2007-1/#comments</comments>
		<pubDate>Wed, 30 Sep 2009 10:00:30 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Book]]></category>
		<category><![CDATA[Science]]></category>
		<category><![CDATA[experiment]]></category>
		<category><![CDATA[Ian Ayres]]></category>
		<category><![CDATA[Reading List]]></category>
		<category><![CDATA[regression]]></category>
		<category><![CDATA[super crunchers]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=1111</guid>
		<description><![CDATA[With the Triumph of Numbers, I read and wrote about the power of using numbers, and how the observation of empirical regularities led to the basic knowledge on how to use such numbers. Already in the triumph of numbers, it ...]]></description>
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		<slash:comments>0</slash:comments>
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		<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 ...]]></description>
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		<slash:comments>0</slash:comments>
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		<title>R-Sessions 25: Book &#8211; Mixed Effects Models in S and S-PLUS (Pinheiro &amp; Bates, 2000)</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-25-book-mixed-effects-models-in-s-and-s-plus-pinheiro-bates-2000/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-25-book-mixed-effects-models-in-s-and-s-plus-pinheiro-bates-2000/#comments</comments>
		<pubDate>Wed, 01 Oct 2008 10:00:51 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Book]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[hierarchical]]></category>
		<category><![CDATA[mixed effects models]]></category>
		<category><![CDATA[multilevel]]></category>
		<category><![CDATA[R-Project]]></category>
		<category><![CDATA[regression]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=628</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>
<img src="http://www.rensenieuwenhuis.nl/wp-content/uploads/2007/07/cover-pinheiro.png" alt="Cover: Mixed-Effects Models in S and S-PLUS" />
Despite the reference to S and S-PLUS in the title of this book, it offers an excellent guide for the nlme-package in R-Project. Reason for this is the close resemblance between R and S. The nlme-package, available in R-Project for estimation of both linear and non-linear multilevel models, is written and maintained by the authors of this book.

]]></description>
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		<title>R-Sessions 23: Book: Data Analysis Using Regression and Multilevel/Hierarchical Models &#8212; Gelman &amp; Hill (2007)</title>
		<link>http://www.rensenieuwenhuis.nl/r-sessions-23-book-data-analysis-using-regression-and-multilevelhierarchical-models-gelman-hill-2007/</link>
		<comments>http://www.rensenieuwenhuis.nl/r-sessions-23-book-data-analysis-using-regression-and-multilevelhierarchical-models-gelman-hill-2007/#comments</comments>
		<pubDate>Tue, 23 Sep 2008 10:00:05 +0000</pubDate>
		<dc:creator>Rense Nieuwenhuis</dc:creator>
				<category><![CDATA[Book]]></category>
		<category><![CDATA[R-Project]]></category>
		<category><![CDATA[R-Sessions]]></category>
		<category><![CDATA[Andrew Gelman]]></category>
		<category><![CDATA[hierarchical]]></category>
		<category><![CDATA[Jennifer Hill]]></category>
		<category><![CDATA[multilevel]]></category>
		<category><![CDATA[regression]]></category>

		<guid isPermaLink="false">http://www.rensenieuwenhuis.nl/?p=608</guid>
		<description><![CDATA[]]></description>
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		<slash:comments>1</slash:comments>
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		<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>3</slash:comments>
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