similar to: Quantile regression (rq) and complex samples

Hi all, I am using quantile regression with svy design. I want to retrieve summary regression statistics (std error, p-value), since I don't have any in my output: Commands: clus1_d<- svydesign(id=~cd002_co, weights=~wtper, strata=~str, data=data) bclus1<-as.svrepdesign(clus1_d,type="bootstrap",replicates=100) fit1<-

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

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

Good afternoon! I'm trying to use the Survey package for a stratified sample which has 4 criteria on which the stratification is based. I would like to get the corrected weights and for every element i get a weight of 1 E.g: tipping design <- svydesign (id=~1, strata= ~regiune + size_loc + age_rec_hhh + size_hh, data= tabel) and then weights(design) gives

Hi, I am trying to fit a model with an ordered response variable (3 levels) and 13 predictor variables. The sample has complex survey design and I've used 'svydesign' command from the survey package to specify the sampling design. After reading the manual of 'svyglm' command, I've found that you can fit a logistic regression (binary response variable) by specifying the

Hi R-users!! I have some trouble with the survey pakage and i would be very glad if you can give me an advice. I have a sample from a survey where household were interviewed. The sample has 4 criteria on which the stratification was based: REGION, SIZE OF HOUSEHOLD, SIZE OF LOCALITY, AGE OF HEAD OF HOUSEHOLD. Since i don't have the whole information in each cell of the cross

I'm trying to learn how to calibrate/postStratify/rake survey data in preparation for a large survey effort we're about to embark upon. As a working example, I have results from a small survey of ~650 respondents, ~90 response fields each. I'm trying to learn how to (properly?) apply the aforementioned functions. My data are from a bus on board survey. The expansion in the

Dear WizaRds, having met some of you in person in Vienna, I think even more fondly of this community and hope to continue on this route. It was great talking with you and learning from you. Thank you. I am trying to work through an artificial example in post stratification. This is my dataset: library(survey) age <- data.frame(id=1:8, stratum=rep(

Dear all. I have a question on plotting the coefficients from a series of mutivariate quantile regressions. The following code plots the coefficients for each RHS variable x1 and x2. Is there a way to plot only the coefficients on x1? In the data I am using, I have a large number of fixed effects and do want to plot the coefficients on these fixed effects. quant.plot <-

Dear all: I have a question about how to get the optimal estimate of coefficients using the penalized quantile regression (LASSO penalty in quantile regression defined in Koenker 2005). In R, I found both rq(y ~ x, method="lasso",lambda = 30) and rq.fit.lasso(x, y, tau = 0.5, lambda = 1, beta = .9995, eps = 1e-06) can give the estimates. But, I didn't find a way using either of

Everyone: I am working on a simulation of the efficiencies of regression estimators when applied to model a specific form of highly skewed data. The outcome variable (y) is being simulated from a generalized lambda distribution (GLD) to reflect the characteristics (mean, variance, skewness, kurtosis) of an observed variable. The regressor of interest (x) is simply a binary indicator of group

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 Dimitris, I have exactly the same problem than you, Do you get some solution? Thanks, Lola Lola Gadea Profesora titular de Economía Aplicada/Lecturer in Applied Economics Universidad de Zaragoza/University of Zaragoza (Spain) lgadea@unizar.es <http://estructuraehistoria.unizar.es/personal/lgadea/index.html>http://estructuraehistoria.unizar.es/personal/lgadea/index.html Grupo de

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

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R While this code run perfectly, it does not work for my data providing a warning message: In rq.fit.sfn(D, y, rhs = a) : tiny

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Is there a way to create a BRR svrepdesign from a survey design when the number of PSUs is odd in one or more stratum? Creating a JKn svrepdesign with that condition works okay, but when I tried to create a svrepdesign with type="BRR" I get an error and this message: "Can't split with odd numbers of PSUs in a stratum" I get that message when I tell it to merge the

I'm working with a 350MB CSV file on a server that has 3GB of RAM, yet I'm hitting a memory error when I try to store the data frame into a survey design object, the R object that stores data for complex sample survey data. When I launch R, I execute the following line from Windows: "C:\Program Files\R\R-2.9.1\bin\Rgui.exe" --max-mem-size=2047M Anything higher, and I get an

Hello, How I use the function as.svrepdesign without memory.size problems? desenho_npc_JK <- as.svrepdesign(desenho_npc,type="JKn") Error: cannot allocate vector of size 161.3 Mb In addition: Warning messages: 1: Reached total allocation of 1022Mb: see help(memory.size) 2: Reached total allocation of 1022Mb: see help(memory.size) 3: Reached total allocation of 1022Mb:

Hi, I have a nested unbalanced data set of four correlated variables. When I do univariate analyses, my factor of interest is significant or marginally significant with all of the variables. Small effect size but always in the same direction. If I do a MANOVA instead (because the variables are not independent!) then my factor is far from being significant. How does that come about? I have