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From |
John Antonakis <John.Antonakis@unil.ch> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Adjusted R-squared comparison |

Date |
Wed, 06 Feb 2013 13:30:35 +0100 |

Hi Panagiotis: In fact, the result you get is the mean and SD of the bootstrap. Specifically: sysuse auto bootstrap e(r2), seed(123) reps(50) : reg price mpg weight gives: Bootstrap replications (50) ----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 .................................................. 50 Linear regression Number of obs = 74 Replications = 50 command: regress price mpg weight _bs_1: e(r2) ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based

-------------+---------------------------------------------------------------- _bs_1 | .2933891 .074451 3.94 0.000 .1474678 .4393104 ------------------------------------------------------------------------------ .2933891 is the mean of the bootstrapped r-squares and .07215 is the SD.

Let's see what others might say. Best, J. __________________________________________ John Antonakis Professor of Organizational Behavior Director, Ph.D. Program in Management Faculty of Business and Economics University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 http://www.hec.unil.ch/people/jantonakis Associate Editor The Leadership Quarterly __________________________________________ On 06.02.2013 12:57, Panagiotis Manganaris wrote:

> > > > 2013/2/6 John Antonakis <John.Antonakis@unil.ch>

> > > Best, > J. > > __________________________________________ > > John Antonakis > Professor of Organizational Behavior > Director, Ph.D. Program in Management > > Faculty of Business and Economics > University of Lausanne > Internef #618 > CH-1015 Lausanne-Dorigny > Switzerland > Tel ++41 (0)21 692-3438 > Fax ++41 (0)21 692-3305 > http://www.hec.unil.ch/people/jantonakis > > Associate Editor > The Leadership Quarterly > __________________________________________ > > On 06.02.2013 11:40, Nick Cox wrote: > That's positive advice. > > My own other idea is that adjusted R-squares are a lousy basis to > compare two models, even of the same kind. They leave out too much > information. > > Nick >

> if their confidence intervals overlap. > > To bootstrap you just do: > > E.g., > > sysuse auto > bootstrap e(r2), seed(123) reps(1000) : reg price mpg weight > > You will be interested in either: > > e(r2_w) R-squared within model > e(r2_o) R-squared overall model > e(r2_b) R-squared between model > > See help xtreg with respect to saved results. > > Let's see if others have other ideas. > On 06.02.2013 10:22, Panagiotis Manganaris wrote: > > I need to compare two adjusted r-squared of the same model for two

> model for each group of data. I've derived their adjusted r-squared and I > want to know if those two adjusted r-squared are significantly different > from each other. > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Adjusted R-squared comparison***From:*Nick Cox <njcoxstata@gmail.com>

**References**:**st: Adjusted R-squared comparison***From:*Panagiotis Manganaris <mangan@econ.auth.gr>

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