similar to: multi-stage sampling and hierarchical models: which packages?

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 expeRts, sorry for such a newbie question - in PCA/factor analysis e.g. in SPSS it is possible to save scores from the factors. Is it analogously possible to "save" the implied scores from the latent variables in a measurement model or structural model e.g. using the sem or lavaan packages, to use in further analyses? Best wishes Steve Powell www.promente.org | skype

hi list members I am trying to use odfWeave with R 2.5.1 on Windows XP. however when running e.g. odfWeave(demoFile, outputFile) I get: Error in odfWeave(demoFile, outputFile) : Error unzipping file In addition: Warning message: unzip not found in: system(zipCmd[2], invisible = TRUE) presumably my zip and unzip are not set up correctly but I dont know how to do that. I installed zip and

Howdy, Referencing the below exchange: https://stat.ethz.ch/pipermail/r-help/2006-April/103862.html I am still getting the same warning ("non-integer #successes in a binomial glm!") when using svyglm:::survey. Using the API data: library(survey) data(api) #stratified sample dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

Dear list members, In sem, std.coef() will give me standardized coefficients from a sem model. But is there a trick so that path.diagram can use these coefficients rather than unstandardized ones? Thanks Steve Powell From: John Fox <jfox_at_mcmaster.ca> Date: Wed 28 Feb 2007 - 14:37:22 GMT Dear Tim, See ?standardized.coefficients (after loading the sem package). Regards, John John

Hello, all. I wanted to use the "survey" package to analyze data from the National Ambulatory Medical Care Survey, and am having some difficulty translating the analysis keywords from one package (Stata) to the other (R). The data were collected using a multistage probability sampling, and there are variables included to identify the sampling units and weights. Documentation from the

I may be doing this wrong! but I have a function which I have simplified a lot below. I want to pass some 'field names' of a data-frame to the function for it to then do some manipulation of. Here's my code: #build a simple dataset mydataset = data.frame (

Hello, I'm running R 2.14.1 on OS X (x86_64-apple-darwin9.8.0/x86_64 (64-bit)), with version 3.28 of Thomas Lumley's survey package. I was using predict() from svyglm(). E.g.: data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) out <- svyglm(sch.wide~ell+mobility, design=dstrat, family=quasibinomial()) pred.df <-

On Thu, Mar 22, 2018 at 4:52 AM, Raja, Dr. Edwin Amalraj <amalraj.raja at abdn.ac.uk> wrote: > Hi , > > library(foreign) > write.dta(data1, "data1.dta") > > should work. I don't think so: > library(foreign) > example(svydesign) > write.dta(dstrat, "~/Downloads/foo.dta") Error in write.dta(dstrat, "~/Downloads/foo.dta") : The

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,

Hi, I am performing a two-way ANOVA (2 factors with 4 and 5 levels, respectively). If I'm interpreting the output of summary correctly, then the interaction between both factors is significant: ,---- | ## Two-way ANOVA with possible interaction: | > model1 <- aov(log(y) ~ xForce*xVel, data=mydataset) | | > summary(model1) | Df Sum Sq Mean Sq F value Pr(>F) |

Dear colleagues, i am carrying out an ordinal regression model. I try it on SPSS but I "flirt" with R as well. I have a few questions. 1. What is the most reliable/tested/trusted package for ordinal regression in the R world? 2. Also, I have a statistical question. What is the danger of having to many 'empty cells' in ordinal regression? How many empty cells are too many? Do

Hi list, I am using the capscale function in vegan_2.0-7 to do a constrained principal coordinates analysis, and I kept getting the following error message: Error in Y.r[, oo, drop = FALSE] : subscript out of bounds I googled but I couldn't find an answer. Could anyone tell me why this error msg and what to do? Here is the command I used:

Dear R users, I was wondering how I can replace the values of a vector with the values from in another vector in the same row For example, how can I replace the value of x below with NA when the value of Z in the same row is NA? x <-1:20 z<- c(11, 15, 17, 2, 18, 6, 7, NA, 12, 10,21, 25, 27, 12, 28, 16,17, NA, 12, 10) Many thanks Mintewab ________________________________________ Fr?n:

Hello all, I have a peculiar and particular bug that I stumbled across with ggplot2. I cannot seem to replicate it with anything other than my specific data set. Here is the problem: - when I try to plot a histogram, allowing for ggplot2 to decide the binwidths itself, I get the following error: - stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to

Dear members of this list, I'd like to count missing values using the aggregate function. Something like this: count_nas <- function(arg1) { return(sum(is.na(arg1))) } aggregate(cbind(var1, var2, var3) ~ subject + time, data = mydataset, count_nas) It's not working: I end up with a matrix containing zeros, although there are missings in the data frame. I'd highly

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

Dear all, I'm trying to use the lattice barchart function to obtain a bar plot of a variable for different values of two factors. The thing I'm trying to do is something like this: > barchart(MyVar ~ season | place, data=mydataset) My problem is that the column MyVar has some negative values and I would like them to be represented as bars stacking down from zero instead of what

Dear R-friends, I use optim(par=c(mystartingpoints), fn=myloglikelihoodfunction, gr=NULL, method=c("L-BFGS-B"), ## I would like to do not use any bounds control=list(trace=6, ## just to see what it's going on maxit=c(20000)), ## to be sure the it doesn't stop reaching the max iterations

Hi there, I am having some trouble with the Identify() function, it looks that the identify() function only works on the plot of 2-dimension data set. In high-dimension case , if I use pairs() + identify() in hope to observe all the components of the data point I selected, I get error message. Is there anyway to get around this ? Plus, I also need it to actually compare all the