similar to: Identification of heterogeneous subpopulation (MECOSA3)

Hi, is there a way to analyze subpopulations (e.g. women over 50, those who answered "yes" to a particular question) in a survey using Survey package? Other packages (e.g. Stata, SUDAAN) do this with a subpopulation option to identify the subpopulation for which the analysis shoud be done. I did not see this option in the Survey package. Is there another way to do this?

Dear members of the R help list, I want to do a hierarchical glm with binomial family but am unsure about how to write the syntax which involves nesting. I want to test whether the risk of being attacked by Herbivores for Meadowsweet plants is significantly dependent on the Distance to heterospecific source plants. Dependent variable = Herbivory (yes/no) Explanatory continuous variable =

I am using R and LISREL for simulation studies. R generates the data that is analyzed with LISREL. In LISREL I use "PV" in the LISREL output statement to request estimated variances. LISREL writes these in a file that looks like this: 1 0 0 0.100331D+01 0.144845D+01 0.141009D+01 0.214423D+01 0.214129D+01 0.194464D+01 0.191531D+01 0.198328D+01 0.100683D+00-0.236392D-01

Dear Helpers   I have a sample frame and i have sampled from it using three methods and now i want to calculate the statistics but i only get the population parameters.   H <- matrix(rnorm(100, mean=50000, sd=5000)) sampleframe=data.frame(type=c(rep("H",100)),value=c(H)) sampleframe   str=strata(sampleframe,c("type"),size=c(20,), method="srswor")

Hello, I am working with version 2.7.2 on a PC and have 2.8.0 available, but have not upgraded completely yet. I have 6 species for which I've run 6 unique environmental management scenarios against for comparison purposes. Each scenario is run for 36 years (1965 - 2000). Some of the species have subpopulations and other do not. For those with subpopulations (A, B, C, D, E, and F) I have

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

Hello to everybody. My name is Carlo and I would like to share with you a problem I encountered. I've tried to install a statistical software, LISREL V.8.8, usually running under Windows. I followed all the Wine procedure by the graphical interface, not command line. The installation seemed complete: at the end of it the installation package asked me to reboot my laptop. I did it twice: the

I''ve a Xen Dom0 with 3 vif-bridges defined, one each for LAN, WAN & DMZ. A DomU router/firewall routes between the 3. Works great. After getting advice from the list, I''ve settled on an apporach to file-services; I''ve launched a storage appliance (OpenFiler) in a DomU. For initial access, it attaches to the LAN bridge. I next want to provide access to the NAS

Hello, I'm using mvpart option xv="1se" to compute a regression tree of good size with the 1-SE rule. To better understand 1-SE rule, I took a look on its coding in mvpart, which is : Let z be a rpart object , xerror <- z$cptable[, 4] xstd <- z$cptable[, 5] splt <- min(seq(along = xerror)[xerror <= min(xerror) + xvse * xstd]) I interprete this as following: the

I created a desktop launcher whose type was a link and pointed to an NTFS file on a read-only ( ro ) partition. When I open the launcher it always asks me whether I want to display or run the file. Of course I want display it and when I click on that button it displays it in my browser, Firefox, correctly. It is neverthless onerous to constantly be asked and have to then click the correct

I don't know of any R package that can match all the functionality of LEM eg fitting equality constraints to model parameters a la LISREL. WRT dumping tables, I would have thought that as.data.frame.table does pretty much what you want, [not tested] newtab <- as.data.frame(table(a,b,c)) cat("dim\n") for(i in seq(1, ncol(newtab)-1) { cat(nlevels(newtab[,1]," ") }

Does anyone know if the sem package in R can implement a stacked model comparison, for example as in LISREL or AMOS? Thanks, Anthony -- Anthony Steven Dick, Ph.D. Post-Doctoral Fellow Human Neuroscience Laboratory Department of Neurology The University of Chicago 5841 S. Maryland Ave. MC-2030 Chicago, IL 60637 Phone: (773)-834-7770 Email: adick at uchicago.edu Web:

Hi Everyone- After searching through posts and my favorite R-help websites I'm still confused about a problem. I have data which is bimodal in nature, but there is no clearly obvious separation between the two peaks. In programs such as Origin, I can deconvolute the two distributions and have it generate a "best guess" as to what the two subpopulations are which make up my

Hi I could only find some discussion on this wrt lattice graphics (which I'm not using). Apologies if I'm missing something obvious. I'd like to produce 3 rug plots under a kernel density plot for a population. The population is subdivided into 3 subpopulations, which I'd like the rug plots to highlight. Naturally, when I do 3 rug plots, they all plot over each other.

A bit late, but you might like to look at http://www.qimr.edu.au/davidD/polyr.R Regarding the original posters queries: You can analyse polychoric correlations as if they were Pearson correlations using standard software (eg sem), and this usually doesn't do too badly, or go to AWLS (Browne) in LISREL etc, or ML analysis of the full multidimensional contingency table using programs such as

Hi, This is a question regarding technique rather than an R specific issue. I have been asked to evaluate a 30+ year long term continuous survey of bird presence/absence data that has an associated ocular estimate of the vegetation community percent coverage. The data are organized by subpopulations (5), and by year ( 1991 - present). We are interested in gaining understanding on whether bird

I would like to solve the equation is is the sum from k = i to N of choose(N,k) * MR ^ k * (1 - MR) ^ (N - k) - 0.50 = 0 I want to solve for MR. This seems like a non-linear equation to me. But I am having a hard time writing the function that implements the above. I could use 'for(...) as a brute force appoarch but I would like a more "elegant" solution. The variables 'N'

I'm new to R - and trying to create a plot similiar to the spider plot at http://www.statgraphics.com/eda.htm#radar . I can't figure out several things... most of which I would think would be straightforward.... How can I change the lines for each series plotted instead of creating a filled area? How can I get the labels for each of the radial axes at the outside of the plot

Hi, have anybody a hint how i can perform my speed for a big reshape jobs? My "long" data.frame have dim 1.342.116 3 and the "wide" data.frame could occur (if i don't filter the timevar) max. in a 246744 x 1444 data.frame with a lot of NA what could be set to 0. Neverthless i didn't need really 1444 columns (idependent's for data-mining) , but