similar to: What does R have for age-adjusted survey analysis?

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

We are pleased to announce version 0.8 of the acs package for R, now available on CRAN (<http://cran.r-project.org/web/packages/acs/index.html>. The package provides a general toolkit for managing, analyzing, and presenting data from the U.S. Census American Community Survey (ACS). Confidence intervals provided with the data are converted to standard errors and bundled with estimates in

We are pleased to announce version 0.8 of the acs package for R, now available on CRAN (<http://cran.r-project.org/web/packages/acs/index.html>. The package provides a general toolkit for managing, analyzing, and presenting data from the U.S. Census American Community Survey (ACS). Confidence intervals provided with the data are converted to standard errors and bundled with estimates in

what does R have to compare with , say , proc surveymeans, estimate survey means/proportions with standard errors, using Taylor methods? [[alternative HTML version deleted]]

I am trying to perform cluster analysis on survey data where each respondent has answered several questions, some of which have categorical answers ("blue" "pink" "green" etc) and some of which have scale answers (rating from 1 to 10 etc).My problem is that certain age groups were over-sampled and I need to weight the data collected in order to accurately reflect the

Hi, Where can I find information ( freely available on the Internet , and also books or other sources ) on how having sampling weights changes the calculation of the standard error (of means and proportions)? How good is R for this type of procedure? And SAS? thanks Robert [[alternative HTML version deleted]]

Hello, all. I wanted to use the "survey" package to analyze data from the National Ambulatory Medical Care Survey, and am having some difficulty translating the analysis keywords from one package (Stata) to the other (R). The data were collected using a multistage probability sampling, and there are variables included to identify the sampling units and weights. Documentation from the

Hi, All Thanks to Robert Baskin, Thomas Lumley, and Spencer Graves for the valuable helps. I have learned a lot from this discussion. I put all discussions together without editing, so we can see how things are evolved. Likely, I have a lot of articles to read. As in the discussion, mixed modeling approach is a poosible but may be over-kill in my posted data analyses. I will explore other

Hello, I have a data set containing categorical and ordinal factors, as well as sampling weights (i.e., survey weights reflecting unequal probabilities of selection). I want to fit a structural equation model with sem(). I have run sem() on weighted covariance matrices using advice from John Fox (see <http://tolstoy.newcastle.edu.au/R/e5/help/08/12/8773.html> and

Hello, I am new to usin R - which is a great tool - and would like to know if R has a seasonal adjustment program for time series and/if it incorporates the Census Bureau's ARIMA x-12 seasonal adjustment program in any way? Thanks so much! Tony [[alternative HTML version deleted]]

Dear WizaRds, I am struggling to compute correctly a cluster sampling design. I want to do one stage clustering with different parametric changes: Let M be the total number of clusters in the population, and m the number sampled. Let N be the total of elements in the population and n the number sampled. y are the values sampled. This is my example data: clus1 <-

Hi all I would like to perform a logistic regression analysis on some complex survey data with R, but am not sure if there are functions within R that will enable me to do so. Also, are there any extensions of the "cor" function that would enable me to incorporate survey weights when calculating correlation coefficients for bivariate data. Any help on this matter will be greatly

Dear WizaRds, Working through sampling theory, I tried to comprehend the concept of stratification and apply it with Survey to a small example. My question is more of theoretic nature, so I apologize if this does not fully fit this board's intention, but I have come to a complete stop in my efforts and need an expert to help me along. Please help: age<-matrix(c(rep(1,5), rep(2,3),

Hello, I'm using the package survey, version 3.22-2 in R 2.11.1, on windows XP, but this isn't a platform issue. I am trying to calculate some regression and probability proportional to size (pps) estimates for a dataset I have. The data has 2 strata, weights, and cluster IDs. However, I can't find a way to use multiple strata in the svydesign function. Currently, I'm ditching

Dear R-help, Let me know if I should email r-devel instead of this list. This message is addressed to Professor Lumley or anyone familiar with the survey package. Does svycoxph() implement the method outlined in Binder 1992 as referenced in the help file? That is, are weights incorporated in the ratio term (numerator and denominator) of the estimating equation? I don't believe so since

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

Hello: I am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey

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

I have survey data that I am working on. I need to make some multi-way tables and regression analyses on the data. After attaching the data, this is the code I use for tables for four variables (sweight is the weight variable): > a <- xtabs(sweight~research.area + gender + a2n2 + age) > tmp <- ftable(a) Is this correct? I don't think I need to use the strata and cluster

I just discovered the great piece of software that is available with the survey package. Many thanks and 'Hats off' to Thomas Lumley. While package survey covers analysis of features of objects sampled (in clusters, strata) I could not find analysis of features of repeated relationsships between sampled objects (in clusters, strata). My understanding is that it is not adequate to treat