similar to: a very little help with sample weights

hi! i have a little problem: my data's matrix has 1093 rows and 3 columns. i'd like to extract each rows.. something like this: ht= my matrix Dt=(???)=a vector with t=1,2...1093 what can i do? thank you! Vincenzo -- View this message in context: http://www.nabble.com/extractor-rows-from-a-matrix-tf3913088.html#a11094459 Sent from the R help mailing list archive at Nabble.com.

>> 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 List, I have a dataset that provides sampling weights (National Survey of Family Growth 2002). I want to produce a cross-tabulation and use the provided sampling weights to obtain representative population estimates. (I believe they are simply frequency weights but codebook is uninformative). I can reproduce results (using this data) that were reported in a recent journal article, if I

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:

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

Hi, I have a supposedly representative sample from a stratified survey. Hence, all observations have an associated sample weight each which inflate the sample to population size. I want to run a glm probit regression on the data but I am not clear about if or better how I can use the weights in it. From the description of the weights argument of glm it seems to me that I cannot plug these

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

Perhaps I am not looking in the right place, but I am looking for a way to use lmer() to run a multilevel model that incorporates sampling weights. I have used the Lumley survey package to use sampling weights in the past, but according to post I found online from Thomas Lumley in mid-2012, R is currently not equipped to be able to do this. His post is here:

Hi all, I've been trying to get a large (12mb) Stata survey database into R. I managed that, but when I attach survey weights, something goes wrong. The error message is: object dchina not found. Here's the script: library(car) library(foreign) library(survey) China <- read.dta("C:/final07c2.dta") attach(China) data(China)

Dear All, I have a complex survey data with observations having differing probabilities of selection into the sample. I would like to run a linear mixed effects model and also to use weighting that takes this into consideration. As far as I know, however, the weighting option for the lme command from nlme package (or glmmPQL from MASS, for that matter) is related to heteroscedasticity. Is there a

Afternoon R help, I want to run Rasch/IRT analyses using the ltm package, however, I am using large scale survey data which requires weighting for accurate results. I attempted to create a weighted object to insert into the formulae of the ltm packages, however, the survey data only includes 30 replicate weights and a sampling weight. The svrepdesign requires additional information such as

R Users I am a new user of R. I have sample weights that I would like to apply to some of the variables in my data set. Where can I go for information on how to do that? Richard

Dear all, First, I would like to thank you for your immense work. My question is about a frequent topic which I am not able to solve - even after hours of search in the mailing lisy. I would like to analyse random-effects (and fixed-effects)models of longitudinal / panel data with sampling weights. I have an unbalanced panel of different individuals in 5 years and income data as well as their

Hi R users! I have a bit of a problem with using an hierarchical clustering algorithm: a<-c(1:15) b<-rep(seq(1:3), 5) c<-rnorm(15, 0,1) d<-c(sample(1:100, 15, replace=T)) e<-c(sample(1:100, 15, replace=T)) f<-c(sample(1:100, 15, replace=T)) data<-data.frame(a,b,c,d,e,f) q<-data.frame(data$d, data$e, data$f) q<-scale(q) What i want to do is to use an

Dear list, I have a data file which includes, alongside various variables representing questionnaire scores, a variable for survey weights computed as the number of observations in the sample drawn from that group divided by the number of observations in the population in the group. I need to calculate a covariance matrix of the questionnaire scores for use in sem. How do I apply the weights?

Hello, I have a data set containing categorical and ordinal factors, as well as sampling weights (i.e., survey weights reflecting unequal probabilities of selection). I want to fit a structural equation model with sem(). I have run sem() on weighted covariance matrices using advice from John Fox (see <http://tolstoy.newcastle.edu.au/R/e5/help/08/12/8773.html> and

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

Hello, I'm estimating an ordered logit model on a probability weighted survey sample. polr permits case weights with the "weights" option, but I cannot figure out from existing documentation what it actually does with these weights. I'm concerned about this because I get somewhat different results using Stata's ologit command with the pweights option and very

Greetings- I am in the process of constructing a nonlinear model using nlme. The model is attempting to fit a nested data structure from some public-opinion data (the data are from individuals nested within organizations). The question I have is fairly simple (I hope). The data were collected in two stages: a 15,000-subject randomly-sampled telephone interview (the SCREEN), with 2,517 subjects

Hi all, I am running a set of logistic regressions, where we want to use some weights, and I am not sure whether what I am doing is reasonable or not. The dependent variable is turnout in an election - i.e. survey respondents were asked whether or not they voted. The percentage of those who say they voted is much higher than the actual turnout, probably due both to non-response bias and social