similar to: Survey Design / Rake questions

I can't figure out the syntax I need to get subset to work. I'm trying to split my dataframe into two parts. I'm sure this is a simple issue, but I'm stumped. I either get all or none of the original "rows". > XTTable <- xtabs( ~ direction_ , SurveyData) > XTTable direction_ EASTBOUND

G'Day R users! Following an ordination using prcomp, I'd like to test which variables singnificantly contribute to a principal component. There is a method suggested by Peres-Neto and al. 2003. Ecology 84:2347-2363 called "bootstrapped eigenvector". It was asked for that in this forum in January 2005 by J?r?me Lema?tre: "1) Resample 1000 times with replacement entire

Hello: I am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey

Dear R-Users, Is anyone aware of a significance test which allows demonstrating that one distribution dominates another? Let F(t) and G(t) be two distribution functions, the alternative hypothesis would be something like: F(t) >= G(t), for all t null hypothesis: F(t) < G(t), for some t. Best wishes, Matthias PS. This one would be ok, as well: F(t) > G(t), for all t null

I'm having a problem reading data to set control totals for a dataframe. I want to adjust a dataframe based on a 2-d table of values, which I get by using : > CurrentX1Sums <- as.matrix(xtabs(~tripid_nu+lineon, data=SurveyData)) > CurrentX2Sums <- apply(CurrentX1Sums, 1, sum) I've created a .csv file with new (target) sums that looks like this: tripid_nu Warner

I want to calculate "expansion factors" for elements in my dataframe based on a 2-d cross classification. Since I'll have "missing values" (many combinations will have no record) I'll need a second "expansion factor" for each "row". I've included my "work to date" below, but I'm not very close to getting this right. My

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

I obviously don't know what I'm doing. I want to create "ByEBNum" as a data frame, but it comes out as a list. How do I make it a data frame? > EBNumStn <- c(673.65, 800, 1000, 1000, 800, 700, 600, 500, 400, 200, 50, 50 ) > ByEBNum <- data.frame(c(1:12),EBNumStn) > typeof(EBNumStn) [1] "double" > typeof(c(1:12)) [1]

Hello, I am running polychoric correlations on a dataset composed of 12 ordinal and binary variables (N =384), using the polycor package. One of the association (between 2 dichotomous variables) is very high using the 2-step estimate (0.933 when polychoric run only between the two variables; but 0.801 when polychoric run on the 12 variables). The same correlation run with ML estimate returns a

I presume the behavior below (no Connector values in the second table) is because I have na values in my dataframe. How do I get them to list? I found references to na.action and na.pass, but I could not figure out how I could use them with xtabs to list the na as a value. I'm sure there's a simple syntax I need, but it's not obvious to me. > # Weighted Data

I'm trying to prepare some cross tabs, looking at a number of variables against a variable "connector" which has 2 values: "OD Passenger" and " Connector". When I produce a xtabs one way I have observations under "Connector" but against a different variable "Connector" shows all 0 values. What is wrong? I've looked into the na commands

> the next tool reading the IR does not like phis when it's generating VHDL. If you're doing a conversion from LLVM IR to some other non-SSA IR (like the tool's), you can do the phi node removal yourself as you convert. Basically, every predecessor block referenced by a phi node will have an assignment to that variable before branching. There are techniques to make the resultant

On 30.8.2011, at 19.19, Eli Friedman wrote: > reg2mem won't do quite this transformation... not sure exactly what you need. I need to get rid of phis. This code is compiled from C++ and for some functions there are no phis, but multiple call instructions. I am targeting hardware in the end, and the next tool reading the IR does not like phis when it's generating VHDL. My questions may

Hi listers, I want to make lattice plots xyplots with the indication of legends inside each panel with only the points and the lines actually ploted inside each given panel according to the group(ing) factor. The code below shows what I have achieved so far and I hope will make clear what I want to have. It seems to me that my solution is a very "dirty hack" and there certainly is

I am trying to combine two data sets, one with daily values and one with weekly values.  SurveyData conatins environmental data collected on a daily basis.  sat.data contains satellite sea surface temperature that is an average of satellite measurements over a six day period.  I would like to combine the two files so that my output file has the daily dates from SurveyData and the weekly average

I'm trying to plot and compare weighted histograms and I can't seem find where to start. I have data similar to this: Miles2LAX RADAM2005Pct LAWA2005Pct 35.57 .00123 .00684 24.74 .00118 .00187 27.09 .00965 .00876 16.23 .00587 .00397 { ...

I have two problems with svyhist. I hope this is easy, and it may be that "simple R syntax" will do what I need. 1) I'm able to get a single plot out, but I need to put two distributions in the same graphic to visually compare them. 2) I get uniform breaks at intervals of 10. I can plot the unweighted data using MyBreaks with hist as shown in the second code snippet

I have a dataframe with many hundreds of survey records containing: tripid_nu, lineon, MuchOtherData, X1, X2, X3, X4 tripid_nu = identifier for each "Trip" {E.g. for EB MTA-901 leaving the first stop at 07:24} lineon = identifier for each of the stop locations Where X1 is the ratio (for each trip/stop combination) of some target number to the number of records.

I want to import some "target values" to normalize elements of a dataframe. I'm summarizing the existing totals using as.matrix(xtabs(~tripid_nu+lineon, data=SurveyData)) and reading a .csv table of target values. I can't even get them to list out as similar data items. My idea is to do something like this, but I don't know how to do it in R

I'm unable to open an SPSS file over my network. If I copy it to my local C:/ drive I can read it. I saved the command (in a "crib sheet" text file) in order to avoid all the typing, so I'm pretty sure I've done it before. I verified that the file I'm trying to read is OK. This is what happens: > SurveyData <-