Re: [R] RuleFit & quantreg: partial dependence plots; showing an effect
Dear Professors Koenker and Varadhan, Thank you for your detailed and engaging replies. The (very) muddy waters clear slowly, but only if I keep moving my hands! Kind regards, Mark Difford. Mark Difford Ph.D. candidate, Botany Department, Nelson Mandela Metropolitan University, Port Elizabeth, SA. - Original Message From: roger koenker <[EMAIL PROTECTED]> To: Mark Difford <[EMAIL PROTECTED]> Cc: R-help list Sent: Wednesday, 20 December, 2006 3:57:02 PM 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/~rogerRoger Koenker email[EMAIL PROTECTED]Department of Economics vox: 217-333-4558University of Illinois fax: 217-244-6678Champaign, 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. > > __ > R-help@stat.math.ethz.ch 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. Send instant messages to your online friends http://uk.messenger.yahoo.com __ R-help@stat.math.ethz.ch 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.
Re: [R] RuleFit & quantreg: partial dependence plots; showing an effect
Thanks, Roger. These should be very useful tools. 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: [EMAIL PROTECTED] Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html -Original Message- From: roger koenker [mailto:[EMAIL PROTECTED] Sent: Wednesday, December 20, 2006 10:59 AM To: Ravi Varadhan Cc: 'Mark Difford'; 'R-help list' Subject: Re: [R] RuleFit & quantreg: partial dependence plots; showing an effect On Dec 20, 2006, at 8:43 AM, Ravi Varadhan wrote: > 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? > > There are some tentative steps in this direction. One is the rqss() fitting in my quantreg package which does QR fitting with additive models using total variation as a roughness penalty for nonlinear terms. Another, along more tree structured lines, is Nicolai Meinshausen's quantregforest package. > > -Original Message- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] 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/~rogerRoger Koenker > email[EMAIL PROTECTED]Department of Economics > vox: 217-333-4558University of Illinois > fax: 217-244-6678Champaign, 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. >> >> __ >> R-help@stat.math.ethz.ch 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. > > __ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help
Re: [R] RuleFit & quantreg: partial dependence plots; showing an effect
On Dec 20, 2006, at 8:43 AM, Ravi Varadhan wrote: > 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? > > There are some tentative steps in this direction. One is the rqss() fitting in my quantreg package which does QR fitting with additive models using total variation as a roughness penalty for nonlinear terms. Another, along more tree structured lines, is Nicolai Meinshausen's quantregforest package. > > -Original Message- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] 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/~rogerRoger Koenker > email[EMAIL PROTECTED]Department of Economics > vox: 217-333-4558University of Illinois > fax: 217-244-6678Champaign, 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. >> >> __ >> R-help@stat.math.ethz.ch 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. > > __ > R-help@stat.math.ethz.ch 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. > > __ > R-help@stat.math.ethz.ch 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. __ R-help@stat.math.ethz.ch 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.
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: [EMAIL PROTECTED] Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] 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/~rogerRoger Koenker email[EMAIL PROTECTED]Department of Economics vox: 217-333-4558University of Illinois fax: 217-244-6678Champaign, 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. > > __ > R-help@stat.math.ethz.ch 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. __ R-help@stat.math.ethz.ch 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. __ R-help@stat.math.ethz.ch 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.
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/~rogerRoger Koenker email[EMAIL PROTECTED]Department of Economics vox: 217-333-4558University of Illinois fax: 217-244-6678Champaign, 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. > > __ > R-help@stat.math.ethz.ch 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. __ R-help@stat.math.ethz.ch 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.
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. __ R-help@stat.math.ethz.ch 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.
