similar to: analysis of data with observation weights

hi, i am a new member. i am using R in finding correlation between two variables of unequal length. when i use cor(x,y,na.rm=T,use="complete") where x has observations from 1928 to 2006 & y has observations from 1950 to 2006. I used na.rm=T to use the "complete observations". So missing values should be handled by casewise deletion. But it gives me error Error in

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

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

Hello to all. My question is very simple... Let's say we have a data frame with three variables (columns): X, W and F. X is a numeric variable (e.g. like income) F is a factor (e.g. with 2 levels) and W is a case weight (data are from household sample but an individual was interviewed, weights are functions of number of persons in the house hold). I wanted to compute a means of X weighted

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

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")

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

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",

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

>> Hi, I am trying to draw a random sample from an household survey with sample weight. Is there any function in R or Splus which allows this. Regards, ******************************************************* Mehtabul Azam Department of Economics Southern Methodist University Dallas TX 75275-0496 Tel: (214) 214 938 3906 Email: mazam at smu.edu <mailto:mazam at smu.edu>

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

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

Dear All, I am doing a growth modeling using NLME. I have three levels in my data: observation, individual, household. About half of my total sample have missing values in my household-level covariates. Under this situation, the best way to go is probably to multiply impute the data (for, say, 5 times), estimate the same model separately on each model using LME function, and merge the results. My

Estimados usuarios: Estoy intentando reproducir el ejemplo 6.4 de Thomas Lumley. Complex Survey. Editorial Wiley. 2010 (ver la página en google:

Hi all, For various reasons I need to have foreign keys with not null constraints. Eg: create_table :people do |t| t.column :name, :string t.column :household_id, :integer, :null => false end create_table :households do |t| t.column :name, :string end add_foreign_key_constraint :people, :household_id, :households, :id So the people table has a foreign key into

Hi All, I am working on an example where the electric utility is investigating the effect of size of household and the type of air conditioning on electricity consumption. I fit a multiple linear regression Electricity consumption=size of the house hold + air conditioning type There are 3 air conditioning types so I modeled them as a dummy variable Type A Type B Type C Where type A is the

Hi all, I've searched everywhere to try to find out how to do this and have had no luck. I am trying to construct identifiers for couples in a dataset. Essentially, I want to identify couples using more than one column as identifiers. Take for instance: obs unit home z sex age 1 015029 18 1 1 053 2 015029 18 1 2 049 3 015029 01 1 1 038 4 015029 01 1