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From |
Jorge Eduardo Pérez Pérez <[email protected]> |

To |
"[email protected]" <[email protected]> |

Subject |
Re: st: Relative Importance of predictors in regression |

Date |
Wed, 6 Nov 2013 09:53:37 -0500 |

You are right about the MS. In my calculations I used the partial SS themselves. These should add up to the R squared. I don't really have much on the way of interpretation of the shared variance, in fact I found that it is hard to interpret it here: http://www.education.umd.edu/EDMS/fac/Harring/Past-Classes/EDMS651/Notes/MRA-MS.pdf -------------------------------------------- Jorge Eduardo Pérez Pérez Graduate Student Department of Economics Brown University On Tue, Nov 5, 2013 at 11:58 AM, David Hoaglin <[email protected]> wrote: > Dear Jorge, > > Thank you for the clarification. Being able to get all the necessary > partial sums of squares from a single command is a big help. > > If we look at the regression coefficients, the step > reg lhs rhs > nicely illustrates the point that I have been making about interpretation. > > reg lhs rhs will give the partial SS for mpg, but the MS from that > command may not have the correct degrees of freedom, because -reg- > does not know about the degrees of freedom that have been partialled > out of price. > > In deriving the part of R-squared attributed to a variable, you need > to use the same denominator as R-squared itself uses. > > Setting aside the example and the calculations for a moment, what is > the definition of "the shared variance of the [in]dependent > variables"? > > Regards, > > David Hoaglin > > On Tue, Nov 5, 2013 at 11:00 AM, Jorge Eduardo Pérez Pérez > <[email protected]> wrote: >> I should have been more specific, sorry, Ignore the SJ paper. >> >> Analysis of variance with continuous covariates and regression are >> general linear models. All these models are equivalent: >> >> sysuse auto, clear >> reg price mpg trunk >> anova price c.mpg c.trunk >> glm price mpg trunk, family(gaussian) link(identity) >> >> >> The anova view of the model will yield the partial sums of squares >> attributed to each regressor. In regression vocabulary, this would be >> the model sum of squares of regressing the dependent variable on each >> one of the independent variables, after partialling out the remaining >> variables. For example, the MS attributed to mpg in the previous >> regression could be obtained as follows, by first removing the >> influence of trunk from both variables: >> >> reg price trunk >> predict lhs, resid >> reg mpg trunk >> predict rhs, resid >> reg lhs rhs >> >> From the anova view, dividing the partial SS of each variable over the >> sum of the partial SS of the variable and the residual will give you >> the part of the R squared attributed to that variable. It will be the >> same as the R squared of the "partialled out" regression I showed >> before. >> The remainder will be the part attributed to the shared variance of >> the dependent variables. >> >> * Part attributed to mpg >> di e(ss_1)/(e(ss_1)+e(rss)) >> * Part attributed to trunk >> . di e(ss_2)/(e(ss_2)+e(rss)) >> * Remainder (shared variance) >> di e(r2) - e(ss_1)/(e(ss_1)+e(rss)) - e(ss_2)/(e(ss_2)+e(rss)) >> >> My suggestion from the anova paper is that the anova view calculates >> all of the sums of squares at once, instead of having to calculate of >> the partial regression sums of squares one by one. >> >> ----------------------------------------- >> Jorge Eduardo Pérez Pérez >> Graduate Student >> Department of Economics >> Brown University > > * > * 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: Relative Importance of predictors in regression***From:*David Hoaglin <[email protected]>

**References**:**st: Relative Importance of predictors in regression***From:*Nikos Kakouros <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*David Hoaglin <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*Lucas <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*David Hoaglin <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*Lucas <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*David Hoaglin <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*Lucas <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*Jorge Eduardo Pérez Pérez <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*David Hoaglin <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*Jorge Eduardo Pérez Pérez <[email protected]>

**Re: st: Relative Importance of predictors in regression***From:*David Hoaglin <[email protected]>

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