Re: [R] RuleFit & quantreg: partial dependence plots; showing an effect

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Re: [R] RuleFit & quantreg: partial dependence plots; showing an effect

 Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables.  The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program appears to do the same thing. Question: Is there a difference between these two types of plot in the manner in which they depict the relationship between explanatory variables and the response variable ? Thank you inav for your help. Regards, Mark Difford. ------------------------------------------------------------- Mark Difford Ph.D. candidate, Botany Department, Nelson Mandela Metropolitan University, Port Elizabeth, SA. ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code. Mark Difford (Ph.D.) Research Associate Botany Department Nelson Mandela Metropolitan University Port Elizabeth, South Africa
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Re: [R] RuleFit & quantreg: partial dependence plots; showing an effect

 They are entirely different:  Rulefit is a fiendishly clever   combination of decision tree  formulation of models and L1-regularization intended to select parsimonious fits   to very complicated responses yielding e.g. piecewise constant functions.  Rulefit   estimates the  conditional mean of the response over the covariate space, but permits a very   flexible, but linear in parameters specifications of the covariate effects on the conditional   mean.  The quantile regression plotting you refer to adopts a fixed, linear specification   for conditional quantile functions and given that specification depicts how the covariates   influence the various conditional quantiles of the response.   Thus, roughly speaking,   Rulefit is focused on flexibility in the x-space, maintaining the classical conditional   mean objective; while QR is trying to be more flexible in the y-direction, and maintaining   a fixed, linear in parameters specification for the covariate effects at each quantile. url:    www.econ.uiuc.edu/~roger            Roger Koenker email    [hidden email]            Department of Economics vox:     217-333-4558                University of Illinois fax:       217-244-6678                Champaign, IL 61820 On Dec 20, 2006, at 4:17 AM, Mark Difford wrote: > Dear List, > > I would greatly appreciate help on the following matter: > > The RuleFit program of Professor Friedman uses partial dependence   > plots > to explore the effect of an explanatory variable on the response > variable, after accounting for the average effects of the other > variables.  The plot method [plot(summary(rq(y ~ x1 + x2, > t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program   > appears to > do the same thing. > > > Question: > Is there a difference between these two types of plot in the manner   > in which they depict the relationship between explanatory variables   > and the response variable ? > > Thank you inav for your help. > > Regards, > Mark Difford. > > ------------------------------------------------------------- > Mark Difford > Ph.D. candidate, Botany Department, > Nelson Mandela Metropolitan University, > Port Elizabeth, SA. > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide http://www.R-project.org/posting-  > guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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Re: [R] RuleFit & quantreg: partial dependence plots; showing an effect

 Dear Roger, Is it possible to combine the two ideas that you mentioned: (1) algorithmic approaches of Breiman, Friedman, and others that achieve flexibility in the predictor space, and (2) robust and flexible regression like QR that achieve flexibility in the response space, so as to achieve complete flexibility? If it is possible, are you or anyone else in the R community working on this? Thanks, Ravi. ---------------------------------------------------------------------------- ------- Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging and Health Division of Geriatric Medicine and Gerontology Johns Hopkins University Ph: (410) 502-2619 Fax: (410) 614-9625 Email: [hidden email] Webpage:  http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html  ---------------------------------------------------------------------------- -------- -----Original Message----- From: [hidden email] [mailto:[hidden email]] On Behalf Of roger koenker Sent: Wednesday, December 20, 2006 8:57 AM To: Mark Difford Cc: R-help list Subject: Re: [R] RuleFit & quantreg: partial dependence plots; showing an effect They are entirely different:  Rulefit is a fiendishly clever   combination of decision tree  formulation of models and L1-regularization intended to select parsimonious fits   to very complicated responses yielding e.g. piecewise constant functions.  Rulefit   estimates the  conditional mean of the response over the covariate space, but permits a very   flexible, but linear in parameters specifications of the covariate effects on the conditional   mean.  The quantile regression plotting you refer to adopts a fixed, linear specification   for conditional quantile functions and given that specification depicts how the covariates   influence the various conditional quantiles of the response.   Thus, roughly speaking,   Rulefit is focused on flexibility in the x-space, maintaining the classical conditional   mean objective; while QR is trying to be more flexible in the y-direction, and maintaining   a fixed, linear in parameters specification for the covariate effects at each quantile. url:    www.econ.uiuc.edu/~roger            Roger Koenker email    [hidden email]            Department of Economics vox:     217-333-4558                University of Illinois fax:       217-244-6678                Champaign, IL 61820 On Dec 20, 2006, at 4:17 AM, Mark Difford wrote: > Dear List, > > I would greatly appreciate help on the following matter: > > The RuleFit program of Professor Friedman uses partial dependence   > plots > to explore the effect of an explanatory variable on the response > variable, after accounting for the average effects of the other > variables.  The plot method [plot(summary(rq(y ~ x1 + x2, > t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program   > appears to > do the same thing. > > > Question: > Is there a difference between these two types of plot in the manner   > in which they depict the relationship between explanatory variables   > and the response variable ? > > Thank you inav for your help. > > Regards, > Mark Difford. > > ------------------------------------------------------------- > Mark Difford > Ph.D. candidate, Botany Department, > Nelson Mandela Metropolitan University, > Port Elizabeth, SA. > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide http://www.R-project.org/posting-  > guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code. ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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