similar to: Strange result in survey package: svyvar

I am facing a problem with a function in survey package. The function svyvar gives the estimated population variance from a given sampling scheme. I am working with a data having more than four continuous variables. In order to have have population total for all those cont. variables I have written in the following format svyvar(~var1+var2+var3+var4+var5+var6,data) ; var1,var2,...,var6 are 6

Hi all- So sorry to bother you all with something pretty basic. I am trying to add the lines method output from svysmooth to a svyplot with style="grayhex". However, the line either appears in the wrong place or if I am running in R Studio it causes the system to crash. I know this is something to do with Lattice graphics, but for the life of me I can not figure out how. Dr. Lumley

Hello, svyvar from the survey package computes variances (with standard errors) from survey design objects. Is there any way to compute standard deviations and their standard errors in a similar manner? Thanks a lot, Sebastian

Hello! I'd like to create an assets-based economic indicator using data from a national household survey. The economic indicator is to be the first principal component from a principal components analysis, which (given the source of the data) I believe should take in consideration the sampling weights of the observations. After running the PCA with svyprcomp(), from the survey package, I

I am having trouble understanding (i.e. getting) confidence intervals from the survey package. I am using R version 2.8.1 (2008-12-22) and survey package (3.11-2) on FC7 linux. To simplify my question I use an example from that package: R> data(api) R> dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) R> (tst <- svyby(~api99, ~stype,

Hi, I can't for the life of me find how to do this in base R, but I'd be surprised if it's not possible. I'm just trying to replace multiple columns at once in a data frame. #load example data data(api) #this displays the three columns and eight rows i'd like to replace apiclus1[ apiclus1$meals > 98 , c( "pcttest" , "api00" , "sch.wide" ) ]

Hi, I am trying to write a function to compute many cross-tabulations with the -svytable- command. Here is a simplified example of the structure of my code (adapted from the -svytable- help file): data(api) func.example<-function(variable){ dclus1<-svydesign(id=~1, weights=~pw,data=apiclus1, fpc=~fpc) svytable(~ variable, dclus1) } When I call this function with:

How do I retrieve the replicates estimates from a crosstab done using svyby? Here is an example from the help page for svyby in the package: > data(api) > dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) > rclus1<-as.svrepdesign(dclus1) > > a <- svyby(~api99, ~stype, rclus1, svymean, return.replicates=TRUE) > a$replicates NULL But, compare to > b

Should the svyby function be able to work with svyquantile? I get the error below ... data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) svyby(~api00, design=dclus1, by = ~stype, quantiles=c(.25,.5,.75), FUN=svyquantile, na.rm=T ) > Error in object$coefficients : $ operator is invalid for atomic vectors A

Hello, Seeking a way to get the variance inflation factor VIF for a model of regression in complex survey, I have understood that without this package (SURVEY) RGui VIF obtained as follows: fit <- lm(mpg~disp+hp+wt+drat, data=mtcars) vif(fit) But I want to know if survey, Vif is obtained so vif( svyglm(api00~ell+meals+mobility, design=dstrat)) Thank you, happy day

svymean w/ svyby is working for me... > svyby(~visitcnt, ~agegrp3.f, svymean, design=svydes) agegrp3.f visitcnt se.visitcnt 18-44 18-44 8.755552 0.4953235 45-64 45-64 10.131555 0.5347806 65+ 65+ 9.588802 0.4323629 svyquantile is working for me... > svyquantile(~visitcnt, quantiles=c(.25, .5, .75), ties="rounded", design=svydes) 0.25

I am working on a new package, one in which the user needs to specify the role that different variables play in the analysis. Where I'm stumped is the best way to have users specify those roles. Approach #1: Separate formula for each special component First I thought to have users specify each formula separately, like: new.function(formula=y~X1+X2+X3, weights=~w,

I get error trying to run examples from 'svyby' help page (?svyby in package "Survey"): # code data(api) dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc) svyby(~api99, ~stype, dclus1, svymean) # error message > Error in match.arg(vartype, several.ok = TRUE) : unused argument(s) (several.ok ...) Is this a version problem? I'm running R

When I use zelig to compute parameter estimates with "model = logit.survey", I receive the following error: Nicholas Carnes. 2007. "logt.surveyWarning message: In eval(expr, envir, enclose) : non-integer #successes in a binomial glm! I believe this is because the model is not using "quasibinomial" (see R-News 2003, Analyzing Survey Data in R, by Thomas Lumley). Is there

Dear R users, I?m student of Master in Statistic and Data analysis, in New University of Lisbon. And now i?m writting my dissertation in variance estimation.So i?m using Survey Package to compute the principal estimators and theirs variances. My data is from Incoming and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of

I have survey data that I am working on. I need to make some multi-way tables and regression analyses on the data. After attaching the data, this is the code I use for tables for four variables (sweight is the weight variable): > a <- xtabs(sweight~research.area + gender + a2n2 + age) > tmp <- ftable(a) Is this correct? I don't think I need to use the strata and cluster

Hi all I would like to perform a logistic regression analysis on some complex survey data with R, but am not sure if there are functions within R that will enable me to do so. Also, are there any extensions of the "cor" function that would enable me to incorporate survey weights when calculating correlation coefficients for bivariate data. Any help on this matter will be greatly

Hello Does anybody happen to know if it is possible to use the survey package to estimate a covariance matrix from a complex survey? I have design weights and clusters (no strata), and want to get a covariance matrix with preferably the effective sample size or else an estimate of the variance-covariance matrix of the covariance matrix ("asymptotic covariance matrix"). Is this

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?

Dear R guru, I am Saikat Sarkar working as a researcher of Economics in Tampere University, Finland. I am trying to estimate some Garch related tests with Bayesian analysis by R programme. I am not good in R but trying to survive. Anyway I have the coding but not working properly. I have tried to find the problem but failed. I am writing to all R gurus to help me out. Could you please look at