similar to: Sample size estimation for sample surveys

Hi, Has anyone done (or know of) any nice R activities that help introductory students ( and teachers :) ) better understand the concepts of simple vs stratified vs cluster sampling? Any links? David -- View this message in context: http://r.789695.n4.nabble.com/SRS-Stratified-and-Cluster-sampling-tp4655099.html Sent from the R help mailing list archive at Nabble.com.

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

I have the final sample weight (expansion factor) from a socieconomic survey. I don't know the exact design used in the study ( (probably is a stratified two-stage design). To illustrate my problem I will use the next dataset which have a sample weight (but the design is not specified) and incorporate the design with svydesign and create some bootstrap replicates in order to be able to

Han-Lin I don't think I have seen a reply so I will suggest that maybe you could try a different approach than what you are thinking about doing. I believe the current best practice is to use the weights as a covariate in a regression model - and bytheway - the weights are the inverse of the probabilities of selection - not the probabilities. Fundamentally, there is a difficulty in making

Hi All, I'm using R's randomForest package (and it's quite awesome!) but I'd really like to do some stratified sampling with a regression problem. However, it appears that the package was designed to only accommodate stratified sampling for classification purposes (see https://stat.ethz.ch/pipermail/r-help/2006-November/117477.html). As Andy suggests in the link just

hi, probably not.. if your survey dataset has a complex design (like clusters/strata), you need to include them in the `svydesign` call. coercing an incorrect survey design into a replicate-weighted design will not fix the problem of failing to account for the sampling strategy On Mon, Apr 4, 2016 at 12:01 AM, Jos? Fernando Zea <jfzeac at gmail.com> wrote: > I have the final sample

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

I have learned a lot from this list. I would like to thank the developers and contributors who devote so much of their time to this project. Does anyone know if any methods have been developed for handling data from complex sample surveys that include sample weights, clusters, strata, and so on? I know that SUDAAN, Stata have some abilities. Does anything exist in R/S? Paul E. Green

>Hello, I am hoping for assistance in regards to examining the contribution of stratified variables in a cox regression. A previous post by Terry Therneau noted that "That is the point of a strata; you are declaring a variable to NOT be proportional hazards, and thus there is no single "hazard ratio" that describes it". Given this purpose of stratification, in the

Howdy, Referencing the below exchange: https://stat.ethz.ch/pipermail/r-help/2006-April/103862.html I am still getting the same warning ("non-integer #successes in a binomial glm!") when using svyglm:::survey. Using the API data: library(survey) data(api) #stratified sample dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

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

Hi R users. I have a problem with function strata in sampling packages. > st0 = strata(dom, stratanames="stratas", size=sample.size, method="systematic",pik, FALSE) Error in sort.list(y) : 'x' must be atomic for 'sort.list' Have you called 'sort' on a list? In previous version of R 2.9.1 and previous version of package sampling this code worked

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

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),

Hi all: For stratified count data,how to perform regression analysis? My data: age case oc count 1 1 1 21 1 1 2 26 1 2 1 17 1 2 2 59 2 1 1 18 2 1 2 88 2 2 1 7 2 2 2 95 age: 1:<40y 2:>40y case: 1:patient 2:health oc: 1:use drug 2:not use drug My purpose: Anaysis whether case and

Rers: What is the preferred library/function for doing stratified random sampling from a dataset, given I want to control the number of samples (rather than the proportion of samples) per strata? Thanks! --j -- Jonathan A. Greenberg, PhD Postdoctoral Scholar Center for Spatial Technologies and Remote Sensing (CSTARS) University of California, Davis One Shields Avenue The Barn, Room

Hi, is there a way to analyze subpopulations (e.g. women over 50, those who answered "yes" to a particular question) in a survey using Survey package? Other packages (e.g. Stata, SUDAAN) do this with a subpopulation option to identify the subpopulation for which the analysis shoud be done. I did not see this option in the Survey package. Is there another way to do this?

Hi, I’m new to R (just installed today) and I’m trying to figure out how to do stratified randomisation using it. My google search expedition has lead me to believe that blockrand package will most probably be the answer to it. I’ve played around with blockrand for awhile and tried the sample code: library(blockrand) ##stratified by sex male <- blockrand(n=100,

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

This is a follow-up to a query that was posted regarding some problems that emerge when running anova analyses for cox models, posted by Mathias Gondan: Matthias Gondan wrote: >* Dear List,*>**>* I have tried a stratified Cox Regression, it is working fine, except for*>* the "Anova"-Tests:*>**>* Here the commands (should work out of the box):*>**>*