similar to: Quantile Regressions/Multi-stage complex survey design

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

Hi everyone, my apologies in advance if I'm overlooking something simple in this question. I am trying to use R's survey package to make a direct method age-adjustment to some complex survey data. I have played with postStratify, calibrate, rake, and simply multiplying the base weights by the correct proportions - nothing seems to hit the published numbers on the nose. I am trying to

I searched archives for how to do quantile regressions with complex survey data, and there was nothing that could be helpful to a first time user. I am looking for equivalent of the functions in "quantreg" package. A vignette and/or examples will be very helpful. Is there someone on this list who has used quantile regressions with survey data? Can you please provide examples of code with

I am new to R and am interested in using the program to fit quantile regression models to data collected from a multi-stage probability sample of the US population. The quantile regression package, rq, can accommodate person weights. However, it is not clear to me that boot.rq is appropriate for use with multi-stage samples (i.e., is capable of sampling primary sampling units instead of survey

Hello, I'm using a complex survey dataset and my goal is to simply spit out a bunch of probability-weighted outcome variable means for the different levels of covariate. So I first define the structure of the study design (I'm using the CDC's NHANES data): dhanes <- svydesign(id=~PSU, strat=~STRATA, weight=~lab_weight, data=final, nest=TRUE) No problem there. Now I use the

Hello all, I am using AddHealth data to fit a cure, aka split population model using nltm. I am not sure how to account for the complex survey design - does anyone have any suggestions? Any help would be greatly appreciated! Sincerely, Sam

For a sanitation project in Bangladesh I need to design a three stage sample survey to be representative of around 40 million people. I find myself suddenly with several challenges with which I struggle and would be gratefully for any help. As the questions are linked I kept them together rather than creating multiple posts 1) SURVEY DESIGN So far I designed mainly two stage cluster surveys but

I am a new to using quantile regressions in R. I have estimated a set of coefficients using the method="br" algorithm with the rq command at various quantiles along the entire distribution. My data set contains approximately 2,500 observations and I have 7 predictor variables. I receive the following warning message: Solution may be nonunique in: rq.fit.br(x, y, tau = tau, ...)

Dear R enthusiasts, I would like to announce nhanesA, a package that enables easy retrieval of the data tables that are available at the National Health and Nutritional Examination Survey (NHANES). NHANES data are used in over 10,000 peer-reviewed journal publications every year. In addition to easy table download, nhanesA features several functions that implement web scraping (using rvest) to

Dear R enthusiasts, I would like to announce nhanesA, a package that enables easy retrieval of the data tables that are available at the National Health and Nutritional Examination Survey (NHANES). NHANES data are used in over 10,000 peer-reviewed journal publications every year. In addition to easy table download, nhanesA features several functions that implement web scraping (using rvest) to

Hello, svyvar from the survey package computes variances (with standard errors) from survey design objects. Is there any way to compute standard deviations and their standard errors in a similar manner? Thanks a lot, Sebastian

I am calling quantiles as follows. I don't understand why sometimes the columns (data values) above 95% are returned as "NULL"!! When I drop the percentile down to 92%, I see colums appearing. Why would any quantile be empty? I see sometimes that 95% percentile is being chosen as "1" for my data between 0 and 1, where obviously there's no column value equal to 1. But

I need some guidance from someone who is familiar/has some experience with the survey package. The data that I am using is from the Medical Expenditure Panel Survey (www.meps.ahrq.gov <http://www.meps.ahrq.gov/> ). The STRATA and PSU variables are varstr01 and varpsu01 respectively. When I try to specify them with the svydesign function I get an error message. An excerpt of my session

I am a physician examining an NHANES dataset available at the NCHS website: http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/demo_d.xpt http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/hdl_d.xpt http://www.cdc.gov/nchs/about/major/nhanes/nhanes2005-2006/tchol_d.xpt Thank you to the R authors and the foreign package authors in particular. Importing from the SAS export

I need a picture like this: http://rwiki.sciviews.org/doku.php?id=graph_gallery:graph38 http://rwiki.sciviews.org/doku.php?id=graph_gallery:graph38 but when I try compile it require("hexbin") data(NHANES)# pretty large data set! good <- !(is.na(NHANES$Albumin) | is.na(NHANES$Transferin)) NH.vars <- NHANES[good,

Hello. I am trying to fit an asymptotic relationship (nonlinear) to some ecological data, and am having problems. I am interested in the upper bound on the data (i.e. if there is an upper limit to 'y' across a range of 'x'). As such, I am using the nonlinear quantile regression package (nlrq) to fit a michaelis mention type model. The errors I get (which are dependant on

I'm wondering if there is a simple way to assign a quantile to a vector in a data frame, much like one could in Stata using centile. Let's say I want 100 slices in my assignation. I can easily see what the limits of each slice by using quantile: quantile(my.df$my.var, probs=seq(0, 1, 0.01)) But how do I assign the appropriate value to each row/record in my data frame? Clearly the

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:

I have looked through the new "Complex Surveys" book and the documentation for the "survey" package and it appears to me that there are no functions in "survey" that help one to design a sampling scheme. For example, in the book section 2.8 discusses the design of stratified samples, but there is no mention of any functions in the "survey" package that

Dear Ladies and Gentlemen, I have a fairly large database (N=13,000) and a single main categorical discriminator between the groups. I want to look at the time course of a number of continuous biochemical variables over chronologic age. Therefore I believe I need to prepare hexbinplots in two columns with simple regression lines in them (with useOuterStrips (in library(latticeExtra) if