similar to: Simple frequencies using svy design

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

Greetings, I have a very, very, simple research question. I want to predict one dichotomous variable using another dichotomous variable. Straightforward, right? The issue is that the dataset has two issues causing some complications for me. 1) The subjects are not independent -- they are sibling pairs. Every person in the dataset has a sibling in the dataset. This needs to be treated a

I have attempted to install the packages Rtools and rgrs but the install.packages command yields a message that the same are not available for R version 4.0. How do i get around this as i need the said packages to implement some credit scoring GLMs using the " rgrs" package. With Regards, Charles [[alternative HTML version deleted]]

Dear list, I got some surprising results when using the svytotal routine from the survey package with data containing missing values. Some example code demonstrating the behaviour is included below. I have a stratified sampling design where I want to estimate the total income. In some strata some of the incomes are missing. I want to ignore these missing incomes. I would have expected that

Dear all, I am looking for an equivalent of the pairwise.t.test() function using a complex survey design. Any suggestions on this or how to account for weights using the pairwise.t.test itself? Thanks, V [[alternative HTML version deleted]]

Hello forum, I want to find in survey how to test V-cramer and coefficient Goodman-Kruskal gamma, to test the collinearity between varibles to enter a model. For the V-Cramer function is used with the package vcd assocstats but without considering the survey package. I appreciate the cooperation. Att. Diana Martinez [[alternative HTML version deleted]]

[url]http://svy.mk/g7RhjY Please take 10 minutes to complete this wine survey. Your feedback will inform a Columbia University graduate student's thesis project for boxed wine. Your answers will be completely anonymous.If you would like to be entered into a prize draw for a $50 Amazon gift card, please provide your email (optional) when you complete the survey. Winner will be notified

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

Hi R-help, I posted about this late yesterday but got no response. I may have put TMI in the original request. Not to mention I couldn't cut and paste yesterday because I was working R off a non-network computer while asking for help on a network computer. Essentially, I have this user-defined function: test <- function(X){ chisq <- svychisq(~X

Dear list, I am trying to set up a propensity-weighted regression using the survey package. Most of my population is sampled with a sampling probability of one (that is, I have the full population). However, for a subset of the data I have only a 50% sample of the full population. In previous work on the data, I analyzed these data using SAS and STATA. In those packages I used a propensity weight

Hi all, I am running a set of logistic regressions, where we want to use some weights, and I am not sure whether what I am doing is reasonable or not. The dependent variable is turnout in an election - i.e. survey respondents were asked whether or not they voted. The percentage of those who say they voted is much higher than the actual turnout, probably due both to non-response bias and social

I am making exploratory analyses on a complex survey data by using survey package. Could you help me how to see the goodness of fit for the model below? Should I use AIC, BIC, ROC, or what? What code would let me run a goodness of fit test for the model? Here are my codes: #incorporating design effects# > mydesign <- svydesign(id=~clust, strata=~strat, weights=~sweight, > data=mydata)

CRAN (and crantastic) updates this week New packages ------------ * disclapmix (0.1) Maintainer: Mikkel Meyer Andersen Author(s): Mikkel Meyer Andersen and Poul Svante Eriksen License: GPL-2 http://crantastic.org/packages/disclapmix disclapmix makes inference in a mixture of Discrete Laplace distributions using the EM algorithm. * EstSimPDMP (1.1) Maintainer: Unknown Author(s):