similar to: Survey - Cluster Sampling

I have the following code which produces the output below it clus1 <- svydesign(ids = ~schid, data = lower_dat) items <- as.formula(paste(" ~ ", paste(lset, collapse= "+"))) rr1 <- svymean(items, clus1, deff='replace', na.rm=TRUE) > rr1 mean SE DEff W525209 0.719748 0.015606 2.4932 W525223 0.508228 0.027570 6.2802 W525035 0.827202

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

I'm trying to use the survey package to calculate a risk difference with confidence interval for binge drinking between sexes. Variables are X_RFBING2 (Yes, No) and SEX. Both are factors. I can get the group prevalences easily enough with result <- svyby(~X_RFBING2, ~SEX, la04.svy, svymean, na.rm = TRUE) and then extract components from the svyby object with SE() and coef() to do the

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 working through Tom Minka's lectures on Data Mining and am now on Day 32. The following is the link: http://alumni.media.mit.edu/~tpminka/courses/36-350.2001/lectures/day32/ In order to use the functions cited I followed the instructions as follows: Installed tree package from CRAN mirror (Ca-1) Downloaded and sourced the file "tree.r" Downloaded the function

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

Hi everyone, apologies if the answer to this is in an obvious place. I've been searching for about a day and haven't found anything.. I'm trying to replicate Stata's confidence intervals in R with the survey package, and the numbers are very very close but not exact. My ultimate goal is to replicate Berkeley's SDA website with R (http://sda.berkeley.edu/), which seems to

Just giving the survey package a spin... I'm accustomed to stata, and it seems very similar in many respects. One thing is throwing me, however. I've gotten my data in, and specified the design. Looks like the weighting is right (based on published population estimates from these data), but now I'd like to check my "marginal means" for proportions against those that have

Dear Colleagues, does anybody know how to add dimnames to an image. Right now I'm using image(as.matrix(df3), col=brewer.pal(9,"Blues")) where df3 is a data.frame. dimnames(as.matrix(df3)) delivers [[1]] [1] "RFM_A1" "RFM_A2" "RFM_A4" "RFM_A5" "RFM_A7" "RFM_B3" "RFM_B6" "RFM_B7"

Is there a way to use one of the functions in the survey package to get a table of estimated percentages (or proportions) and the standard error for each? For example, suppose that AGECODE AND SEX are two factors with 5 and 2 levels. The 5x2 AGECODE x SEX table would have the estimated percentage of persons in each cell, 100*(sum of weights in the cell) / (sum of all weights) and the std

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

Greetings R-users, I have been using the fpc package in R to cluster my data. Speficically I am using kmeansruns clustering. I would like to know how I use R to partition data into clusters. What I am doing is as follows. # Use csv file as input ##################### wholeset = read.csv("Spellman800genesImputed.csv") # exclude first col (gene names) ##########################

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

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:

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),

Hi, I have been experimenting with the new Survey package. Specifically, I was trying to use some of the functions on the public-use survey data from NHIS (2000 Sample Adult file). Error 1): The first error I get is when I try to specify the complex survey design. nhis.design<-svydesign(ids=~psu, probs=~probs, strata=~strata, data=nhis.df, check.strata=TRUE) Error in svydesign(ids =

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

Dear List: I am working to understand some differences between the results of the svymean() function in the survey package and from code I have written myself. The results from svymean() also agree with results I get from SAS proc surveymeans, so, this suggests I am misunderstanding something. I am never comfortable with "I did what the software" does mentality, so I am working to

Hi, I'm trying to get standard errors for some of the variables in my data frame. One of the questions on my survey is whether faculty coordinate across curriculum to include Arts Education as subject matter. All the responses are coded in zeros and ones obviously. For some of the other variables I have a 2 for those that responded with "Don't Know". I'm getting NA for

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