Thanks very much, Greg. I will certainly look at glmpath. My goal is to develop (nearly) automatic and flexible procedures for estimating causal effects of risk factors in observational epidemiological studies. A major part of this is the development of a propensity score model (when the exposure is binary). I would like to use tools/approaches that can do this semi-automatically so that the resulting model has both low prediction error and good covariate balance.
I have read your paper (McCaffrey, Ridgeway and Morral 2004), which uses a gradient boosting machine (gbm) to build a logistic regression model for propensity score. I was wondering whether there are other tools that can also address this problem, for example, glmpath or MARS? An important question is whether these "machine learning" methods, mainly focused on a good prediction rule, can also achieve a good covariate balance between the treatment groups, since "balance" is not explicitly built into the cost function. If there is significant imbalance, incorporating such covariates into the regression model for outcomes, and performing a weighted least squares analysis (with estimated propensity score as weights) should be reasonable. Am I right? I would appreciate comments on these points. Thanks very much. Best, 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: Ridgeway, Greg [mailto:[EMAIL PROTECTED] Sent: Monday, September 18, 2006 2:17 PM To: [email protected] Cc: Ravi Varadhan Subject: Re: [R] LARS for generalized linear models Check out Park & Hastie's glmpath package. They have a really clever analysis and implementation of a generalized least angle regression. Greg >On Fri, 2006-09-15 at 18:49 -0400, Ravi Varadhan wrote: > > Is there an R implementation of least angle regression for binary response > > modeling? I know that this question has been asked before, and I am also > > aware of the "lasso2" package, but that only implements an L1 penalty, i.e. > > the Lasso approach. > > > Madigan and Ridgeway in their discussion of Efron et al (2004) describe a > > LARS-type algorithm for generalized linear models. Has anyone implemented > > this in R? -------------------- This email message is for the sole use of the intended recip...{{dropped}} ______________________________________________ [email protected] 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.
