search for: poststratified

...ean , na.rm = T ) svyby( ~HI_CHOL , ~RIAGENDR+race , z , svymean , na.rm = T ) # but matching the figure exactly requires an exact age adjustment. # create the population types vector pop.types <- data.frame( agecat = 0:3 , Freq = c( 55901 , 77670 , 72816 , 45364 ) ) z.postStratified <- postStratify( z , ~agecat , pop.types , partial = T ) # these estimates are close, but not exactly right. svymean( ~HI_CHOL , z.postStratified , na.rm = T ) svyby( ~HI_CHOL , ~race , z.postStratified , svymean , na.rm = T ) svyby( ~HI_CHOL , ~RIAGENDR , z.postStratified , svymean , na.rm = T...

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 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

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(

I am new to R and am interested in using the program to fit quantile regression models to data collected from a multi-stage probability sample of the US population. The quantile regression package, rq, can accommodate person weights. However, it is not clear to me that boot.rq is appropriate for use with multi-stage samples (i.e., is capable of sampling primary sampling units instead of survey

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

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

Estimados usuarios de R: Estoy usando un programa en que utilizo la función rake: *rake(ps,list(~A,~B,~C),list(pop.A, pop.B, pop.C),control = list(maxit=1000, epsilon = 1, verbose=TRUE))* ** *Obtengo como resultado:* ** , , C = 1 B A 1 2 3 4 5 A1 0.000000 0.000000 0.000000 0.000000 0.000000 A2 3.000000 3.000000 0.000000