search for: stratif

Hello, Does R have any capabilities (or are there any add on packages) which can do estimation of standard statistical models (means, regression, logistic regression, etc) which take into account not only weights (e.g. post-stratification weights) but also the sample design, such as stratification and clustering information (to compute a robust taylor linearized variance estimator, for example)? Thanks much for any input, Jason _______________________________ Jason C. Bond, Ph.D. Biostatistician, Associate Scientist...

Hello everyone,   I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable.   Hypothetical example:   group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F)   Our data would be split into:   Fair, male, relapse Dark,male, relapse Fair, female, relapse Dark, female, relapse   Fair, male, TRM Dark,male, TRM Fair, female, TRM...

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( c("S1","S2"),c(5,3)), weight=rep(c(3,4),c(5,3)), nh=rep(c(5,3),c(5,3)), Nh=rep(c(15,12),c(5,3)), y=c(23,25,27,21,22, 77,72,74) ) pop.types <- table(stratum=age$stratum) age.post <- sv...

Hi. I'm a student at SFU in Canada. The basic thing I want to do is calculate means of different strata. I have 2 vectors. One has the values I want to take the means from, the other is the four strata I am interested in. So I essentially want to break up the information vector into the four strata and calculate four means, one for each stratum. How can I do this in a reasonable way? Thanks

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant?...

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,...

...ein I have information on the type of business, as well as, for designated employment number bands, number of employees and business turnover information. So in this context the employment number bands can be described as micro, small, medium and large, i.e. size of business. Hence I would like the stratification to be business type X business size. I note that SamplingStrata allows for multivariate scenarios and the data frame should be straightforward to set up, but in terms of "domains" which descriptor should be used for this? I am assuming I can use both number of employees and busines...

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:

...r example didn't demonstrate the problem because the variable that interacted with strata (zed) was not a factor variable. But I had stated the problem incorrectly. It's not that there are too many strata terms; there are too many non-strata terms when the variable interacting with the stratification factor is a factor variable. Here is a simple example, where I have attached no packages other than the basic startup packages. strat <- function(x) x d <- expand.grid(a=c('a1','a2'), b=c('b1','b2')) d$y <- c(1,3,2,4) f <- y ~ a * strat(b) m &l...

...r example didn't demonstrate the problem because the variable that interacted with strata (zed) was not a factor variable. But I had stated the problem incorrectly. It's not that there are too many strata terms; there are too many non-strata terms when the variable interacting with the stratification factor is a factor variable. Here is a simple example, where I have attached no packages other than the basic startup packages. strat <- function(x) x d <- expand.grid(a=c('a1','a2'), b=c('b1','b2')) d$y <- c(1,3,2,4) f <- y ~ a * strat(b) m &l...

Hello everyone, I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable. Hypothetical example: group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F) Our data would be split into: Fair, male, relapse Dark,male, relapse Fair, female, relapse Dark, female, relapse Fair, male, TRM Dark,male, TRM Fair, female, TRM...

...d they should be displayed in the right box (based both on the "ERBB2.2064" field and "ERBB2_Status"). However, given my command I currently only see "red" points corresponding to "MUT" subset in one straight line corresponding to only "ERBB2.2064" stratification on x-axis. It dosen't take into account the "ERBB2.Status" stratification. Can anyone help me? Call ERBB2|2064 ERBB2_Status ERBB2-MUT A 7.214E-01 CHANGE MUT B -4.208E-02 NEUTRAL MUT D 1.080E+00 NEUTRAL MUT C 2.347E-01 NEUTRAL MUT ggplot(data=testdata, aes(x=Call, y=ERBB2.2064...

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 region*sizehh*sizeloc*age i can't use the postStratify function from Survey package. Is that correct? (I think so but i need a competen...

...on on the type of > business, as well as, for designated employment number bands, number of > employees and business turnover information. So in this context the > employment number bands can be described as micro, small, medium and large, > i.e. size of business. Hence I would like the stratification to be business > type X business size. I note that SamplingStrata allows for multivariate > scenarios and the data frame should be straightforward to set up, but in > terms of "domains" which descriptor should be used for this? I am assuming > I can use both number of e...

...; business, as well as, for designated employment number bands, number of >> employees and business turnover information. So in this context the >> employment number bands can be described as micro, small, medium and >> large, >> i.e. size of business. Hence I would like the stratification to be >> business >> type X business size. I note that SamplingStrata allows for multivariate >> scenarios and the data frame should be straightforward to set up, but in >> terms of "domains" which descriptor should be used for this? I am assuming >> I...

Using R version 1.8.1 for Windows, I obtain an error message using the following code. The data frame was constructed in the counting process style, where V1 is the start time, V2 is the stop time, and V3 is the censoring indicator. There are no zero-length time intervals. Variable V4 is the stratification factor (gender: F,M). S<-Surv(V1,V2,V3) fit<-survfit(S ~ V4,data=test.dat) summary(fit) produces the following error message: Call: survfit(formula = S ~ V4) V4=F Error in as.matrix(x) : (subscript) logical subscript too long Using no stratification factor in the...

Hi R-users, Can anyone tell me where can i find info about they way how post stratification weights are calculated when i have an already stratified survey design, especially in Survey Package (but any theoretical material would do me just fine) ? Thank you and have a nice day! --------------------------------- [[alternative HTML version deleted]]

>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...

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 weig...

Hi all, i am looking to built a simple example of a very basic propensity score adjustment, just using the estimated propensity scores as inverse probability weights (respectively 1-estimated weights for the non-treated). As far as i understood, MLE predictions of a logit model can directly be used as to estimates of the propensity score. I already considered the twang package and the