similar to: R survey package again

Hi dears, I expect that Mr Thomas Lumley will read this message. I have data from a complexe stratified survey. The population is divide in 12 regions and a region consist to and urban area and rural one. there to region just with urbain area. stratification variable is a combinaison of region and area type (urban/rural) In rural area, subdivision are sample with probabilties proporionnal to

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'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

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 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

Dear all, I was wondering whether anyone knows about a program which converts (part of) a data definition file (such as a .dct file for stata, or .sas file for sas) into an R-script. Here is an example with a .sas file: <-- snipp VALUE HTYPE (default=32) 0 = "Not in household" 1 = "Married couple family household" 2 =

Dear R users, I am trying to convert a dataset from one format to several rectangular datasets. A consultant helped design the data entry program for our survey using Delphi/Pascal and for each household the information is stored in a file called "EA-HM-HH.TXT" where EA is the enumeration area number, HM is the homestead number and HH is the household number. Within this file the

Dear colleagues, I'm working with the 2008 Canada Election Studies (http://www.queensu.ca/cora/_files/_CES/CES2008.sav.zip), trying to construct a weighted national sample using the survey package. Three weights are included in the national survey (a household weight, a provincial weight and a national weight which is a product of the first two). In the following code I removed variables with

I have a people table with four types of people: clients, spouses, children, and others all setup using single-table inheritance with a foreign key back to a household record. A Household has_one client and spouse, and has_many children and others. I want to use a single "Household.find(@session [:household_id], :include => [:client, :spouse, :children, :others])"

Hello, I need some help in creating a new variable. I need to create a 'couple identifier', which gives a unique code for every couple/triple/... in a household. So, I can identify couples. To do this, I should use 4 variables: * SERIAL = a unique numeric code for each household * PERNUM = a unique numeric code for each person * SPLOC = the numeric code of the spouse in the

Dear all, sorry, this is for sure really basic, but I searched a lot in the internet, and just couldn't find a solution. The problem is to create new variables from a data frame which contains both individual and group variables, such as mean age for an household. My data frame: df hhid h.age 1 10010020 23 2 10010020 23 3 10010126 42 4 10010126 60 5 10010142

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),

Dear R-users, Recently I had to analyze a dataset from household survey. The sample design ensured, that each household in the population has the same probability of being sampled. However the data were gathered from only one adult individual in each household, who was randomly choosen by an interviewer (via "Kish grid"). To equalize the probabilities for each INDIVIDUAL a casewise

Hi all, I have a dataset of individuals where the variable ID corresponds to the identification of the household where the individual lives. rel.head stands for the relationship with the household head. so rel.head=1 is the household head, rel.head=2 is the spouse, rel.head=3 is the children. Here is an example to see how it looks like: df<-data.frame(ID=c("17100",

Dear All, I'm analysing a negative binomial dataset from a population-based study. Many covariates were determined on household level, so all members of a household have the same value for those covariates. In STATA, there seems to be an option for 'clustered analysis' for neg-bin regression. Does an equivalent exist for R(MASS)'s glm.nb or a comparable function? Many thanks for

Good afternoon! As mentioned in the subject, my question regards more the methodological part that accompanies survey design and the statistical part that is involved. So, I have the following data: a<-data.frame (id_hh=c(1:5), strata=c(1,1,2,2,1), Nhstrata=c(100,100,200,200,100), Nrmemb=c(2,4,2,5,4)) a$ocmemb1<-c("wk","jl","st","jl","st")

Hi, I am trying to run a logistic regression to look at the risk of malaria infection in individuals. I want to account for intra household correlation and so want to include a household level random effect. I have been using the lmer command in lme4 package but am getting some strange results that are completely different to those I get using STATA. Can I just check that this is the correct

I have the following sample dataset (CSV input here:http://goo.gl/YR8LP. CSV output here: http://goo.gl/EFCC8) which I want to transform as follows. For each person in a household I want to create two new variables OrigTAZ and DestTAZ. It should take the value in TripendTAZ and put that in DestTAZ. For OrigTAZ it should put value of TripendTAZ from the previous row. For the first trip of every

I would appreciate help in knowing how to repeat categorical variable code given in column=A, by the number in a matching column=B. For example, I have a categorical variable code attributed to a household=A and want to replicate the code for all member of the household, as given in column=B. I would like to have one sequence of categorical variable codes for individuals in column C. I have ~9000

Hello, I am new to R. I would like others to explain to me how to add absolute values inside the individual stacked bars in a consistent way using the basic R plotting function (R base). I tried to plot a stacked bar graph using R base but the values appear in an inconsistent/illogical way in such a way that its supposed to be 100% for each village but they don't sum up to 100%. Here is the