similar to: Using R for a slightly tricky survey analysis

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

Hi all, I have data collected from a survey administered on a subset of the population. I also have the population proportions of variables such as gender, race and housing type. I would like to combine the weights from each separate cross tab (of gender, race and housing type) such that the weighted proportions of my survey data matches that of the population. I have tried the following:

Dear R-Helpers, SAS Institute just mailed out the notice below regarding a survey of people who do data mining. To help keep the survey from becoming biased toward commercial software, I thought it would be good to post it here as well. Cheers, Bob Fourth Annual Data Miner Survey Rexer Analytics has asked statistical and data mining software vendors to forward this survey as a courtesy. (SAS is

Greetings to all of the Shorewall community! We''d like to find out a little more about the environments in which Shorewall runs, and to this end i''ve created a survey. It is mostly designed to allow Shorewall users to see how their environment compares with that of the average Shorewall user (if such a thing exists!), but the results may be used by the Shorewall team to assist

Hi R users, I am trying to use the aggregate function with a survey design object and survey functions, but get the following error. I think I am incorrectly using the syntax somehow, and it may not be possible to access variables directly by name in a survey-design object. Am I right? How do I fix this problem? I have used aggregate with "mean" and "weighted.mean", and

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

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

What Samba features do you care about most? Do you have problems or ideas to tell the Samba Team? Which parts of the documentation are most important to you? The Samba Team invites all users to participate in the Samba Survey at https://www.surveygizmo.com/s3/2020369/Samba-User-Survey-2015 The survey takes only 5-10 minutes to complete and runs until the end of March. We don't of course

What Samba features do you care about most? Do you have problems or ideas to tell the Samba Team? Which parts of the documentation are most important to you? The Samba Team invites all users to participate in the Samba Survey at https://www.surveygizmo.com/s3/2020369/Samba-User-Survey-2015 The survey takes only 5-10 minutes to complete and runs until the end of March. We don't of course

Dear Kevin, In addition to the advice you've received, take a look at the survey package. It's not quite what you're asking for, but in fact it's probably more useful, in that it provides correct statistical inference for data collected in complex surveys. The package is described in an article, T. Lumley (2004), Analysis of complex survey samples, Journal of Statistical Software

Dear R-Users! Is there a possibility in R to do analyze longitudinal survey data (repeated measures in a survey)? I know that for longitudinal data I can use lme() to incorporate the correlation structure within individual and I know that there is the package survey for analyzing survey data. How can I combine both? I am trying to calculate design-based estimates. However, if I use svyglm() from

Dear R users, I?m student of Master in Statistic and Data analysis, in New University of Lisbon. And now i?m writting my dissertation in variance estimation.So i?m using Survey Package to compute the principal estimators and theirs variances. My data is from Incoming and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of

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

Survey: "Motivation of Free/Open Source Software (F/OSS) Developers" We (Marc R?ttig and Carl-Daniel Hailfinger) are currently working on a survey on the motivation of open source developers as part of a "Computer Science and Society" project at the CS department of the University of T?bingen. We invite every developer in the Free / Open Source Software community to help us

First the R question. I have the results of a rather large survey (thousands of forms, each with dozens of questions) with some existing weights and expansion factors. I wish to add additional weighting factors, based on new information that elements of certain variables should appear in certain proportions. Where should I look in R to develop what is essentially an N-dimensional balancing

Hi, is there a function in R equivalent to the "weight by"-command in SPSS? I'm working with survey datasets and the data need to be weighted by a survey/ probability/ design weight (to compensate for different probabilities to be included in the sample in each country). The weight variables are included in the datasets. I did find functions in the Hmisc and survey packages that

Hi, I'm trying to get standard errors for some of the variables in my data frame. One of the questions on my survey is whether faculty coordinate across curriculum to include Arts Education as subject matter. All the responses are coded in zeros and ones obviously. For some of the other variables I have a 2 for those that responded with "Don't Know". I'm getting NA for

Good afternoon! I'm trying to use the survey package to get a better point of view for my data, but i need some piece of advice: i have some data from a survey which has been stratified using 2 criteria: region(7 values), size of locality(5 values) Using the survey pakage how can i define in a correct way this design (taking into account all 4 strata not just one as in the Survey

An organization has asked me to comment on the validity of their recent all-employee survey. Survey responses, by geographic region, compared with the total number of employees in each region, were as follows: > ByRegion All.Employees Survey.Respondents Region_1 735 142 Region_2 500 83 Region_3 897 78

Hello List, Could someone tell why I can't install the Himsc and survey packages for R version 2.13.0 (2011-04-13)? What am I doing wrong here? Thanks, Pradip > install.packages ("Hmisc", dependencies=TRUE) --- Please select a CRAN mirror for use in this session --- Warning: unable to access index for repository http://watson.nci.nih.gov/cran_mirror/bin/windows/contrib/2.13