similar to: Survey data: Quantile Regressions

Hello, I am utilizing linear quantile regression models to analyze health survey data. The survey (NHANES) is a multi-stage complex survey and I want to incorporate survey sampling weights in generating my quantile estimates. To my knowledge, this is currently not possible in SAS or STATA. Is there any way to do this in R? If so, how? Thanks! -Kish

I've been looking for ways to calculate a large number (100) of non-crossing Nonparametric quantile regressions on large populations (1000+). Can the quantreg package in R ensure the non-crossing property? If not, do you know any alternative? Thank you, Paulbegc -- View this message in context:

The help page and vignette for summary.rq(quantreg) mention that there are three different bootstrap methods available for the se="bootstrap" argument, but I can't figure out how to select a particular method. For example, if I want to use the "xy-pair bootstrap" how do I indicate this in summary.rq? Tom -- View this message in context:

Hi, how to measure the goodness of fit, when using the rq() function of quantreg? I need something like an R^2 for quantile regression, a single number which tells me if the fit of the whole quantile process (not only for a single quantile) is o.k. or not. Is it possible to compare the (conditional) quantile process with the (unconditional) empirical distribution function? Perhaps with a Chi^2

Dear there, I am working on the NHANES survey data, and want to apply quantile regression on these complex survey data. Does anyone know how to do this? Thank you in advance, Yiling Cheng Yiling J. Cheng MD, PhD Epidemiologist CoCHP, Division of Diabetes Translation Centers for Disease Control and Prevention 4770 Buford Highway, N.E. Mailstop K-10 Atlanta, GA 30341 [[alternative HTML

All - Does anyone know if there is a method to calculate a goodness-of-fit statistic for quantile regressions with package quantreg? Specifically, I'm wondering if anyone has implemented the goodness-of-fit process developed by Koenker and Machado (1999) for R? Though I have used package quantreg in the past, I may have overlooked this function, if it is included. Citation: Koenker, R. and

Mike, Do something like: require(rms) dd <- datadist(mydatarame); options(datadist='dd') f <- Rq(y ~ rcs(age,4)*sex, tau=.5) # use rq function in quantreg summary(f) # inter-quartile-range differences in medians of y (b/c tau=.5) plot(Predict(f, age, sex)) # show age effect on median as a continuous variable For more help type ?summary.rms and ?Predict Frank ------------

Hello Everyone, I'm currently learning about quantile regressions. I've been using an optimizer to compare with the rq() command for quantile regression. When I run the code, the results show that my coefficients are consistent with rq(), but the intercept term can vary by a lot. I don't think my optimizer code is wrong and suspects it has something to do with the starting

Dear all, I am searching for a way to compute a test comparable to Chuang et al. ("Causality in Quantiles and Dynamic Stock Return-Volume Relations"). The aim of this test is to check wheter the coefficient of a quantile regression granger-causes Y in a quantile range. I have nearly computed everything but I am searching for an estimator of the density of the distribution at several

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

Is there a way around bounce back? The moderator has not updated his email address. ---------- Forwarded message ---------- From: Postmaster <systems-postmaster@world.deshaw.com> Date: Thu, Apr 4, 2013 at 11:17 AM Subject: Important: message being returned. To: anupamtg@gmail.com Thank you for your inquiry. Miheer Kulkarni is no longer with the firm. For immediate assistance, please

Hi All, I am running quantile regression in a "for loop" starting with 1 variable and adding a variable at a time reaching a maximum of 20 variables. I get the following warning messages after my "for" loop runs. Should I be concerned about these messages? I am building predictive models and am not interested in inference. Warning messages: 1: In

Hello, I?m wondering if anyone can offer advice on the out-of-memory error I?m getting. I?m using R2.12.2 on Windows XP, Platform: i386-pc-mingw32/i386 (32-bit). I am using the quantreg package, trying to perform a quantile regression on a dataframe that has 11,254 rows and 5 columns. > object.size(subsetAudit.dat) 450832 bytes > str(subsetAudit.dat) 'data.frame': 11253 obs.

R-ers: I was wondering if anyone had suggestions on how to implement a GAM in a quantile fashion? I'm trying to derive a model of a "hull" of points which are likely to require higher-order polynomial fitting (e.g. splines)-- would quantreg be sufficient, if the response and predictors are all continuous? Thanks! --j

I am trying to make plots that take into account survey weights. This a survey of the US population. To start with I want to explore the data using pairs, plot, coplots and lattice. Are there specialized methods that handle survey weights for plotting? Any pointers? Anupam. [[alternative HTML version deleted]]

Please, I have a problem with nonlinear quantile regression. My data shows a large variability and the quantile regression seemed perfect to relate two given variables. I got to run the linear quantile regression analysis and to build the graph in the R (with quantreg package). However, the up part of my data dispersion seems a positive exponential curve, while the down part seems a negative

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'

I am using the quantreg package to build a quantile regression model and wish to generate confidence intervals for the fitted values. After fitting the model, I have tried running predict() and predict.rq(), but in each case I obtain a vector of the fitted values only. For example: library(quantreg) y<-rnorm(50,10,2) x<-seq(1,50,1)

Greetings does anyone know of an R package that can do quantile regression with instrumental variables. I've found 'sem' for IV estimation and 'quantreg' for quantile regression but I would like to find something that can do a quantile regression with instrumental variables. Cheers, Neil ============================================= Neil Hepburn, Economics Instructor

Dear list, I'm using the quantreg package for quantile regression. Although it's fine, there're is some weird behavior a little bit difficult to understant. In some occasions, the regression results table shows coefficients, t-statistics, standard errors and p-values. However, in other occasions it shows only coefficients and confidence intervals. Therefore, the question is... Is