similar to: Multinomial Regression for Complex Survey

Hello, Is there a function/package that can do multinomial logistic regression using survey weights, similar to "svymlogit" in Stata? It appears that only "svyglm" function (which does not allow multinomial response?) is available in the "survey" package. Thank you! Masha Kocherginsky

Hello everyone, I know where is wrong. I forget to specify the parameters in my function. Thank you for anyone who was trying to help me! Best, Yen ?? b88207001 at ntu.edu.tw: > Hello everyone, > > It seems that I was not successfully attached the code. Here is the > code. I appreciate any help! > > Best, > Yen > > ?? b88207001 at ntu.edu.tw: > >> Hello

Dear R-list members, I´m using the package "survey" and I need to find a function for multinomial logistic regression in a complex design. The functions that I see are only for dicotomic and ordinal variables. Thank you! Rosario Austral ________________________________ De: "r-help-request@r-project.org" <r-help-request@r-project.org> Para:

Hello everyone, It seems that I was not successfully attached the code. Here is the code. I appreciate any help! Best, Yen ?? b88207001 at ntu.edu.tw: > Hello everyone, > > I have been working on a code which simply repeatedly appends a > number into a vector and write a file. However, it could not be > properly implemented when I use it. It works when I run it line by

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

Hello everyone, I have been working on a code which simply repeatedly appends a number into a vector and write a file. However, it could not be properly implemented when I use it. It works when I run it line by line. I wonder what is the problem and I appreciate anyone who is willing to help. The function and the example code is attached. Any advice is appreciated! Best, Yen

Hello dear uesRs, I am working on modeling both level one and level two heteroscedasticity in HLM. In my model, both error variance and variance of random intercept / random slope are affected by some level two variables. I found that nlme is able to model heteroscedasticity. I learned how to use it for level one heteroscedasticity but don't know how to use it to model the level

Greetings, I am using library(survey) to analyze some complex sample data. After fitting a model I tried to use termplot() with make.panel.svysmooth(), but I received an error (see below). Could someone help me interpret the error message so I can make the necessary corrections? The make.panel.svysmooth() function seems to work fine, and termplot() worked fine after I dropped the smoother.

If you don't get a response it is because you did not read the Posting Guide which indicates that the R-sig-ME mailing list is where this question would have been on-topic. -- Sent from my phone. Please excuse my brevity. On August 16, 2017 6:17:03 AM PDT, b88207001 at ntu.edu.tw wrote: >Hello dear uesRs, > >I am working on modeling both level one and level two

A better place for this post would be on R's mixed models list: r-sig-mixed-models . Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Aug 16, 2017 at 6:17 AM, <b88207001 at ntu.edu.tw> wrote: > Hello dear

Dear all, Does someone know an R function implementing the method of Sison and Glaz (1995) (see full ref below) for computing Simultaneous confidence intervals for multinomial proportions? As alternative method, I think to boostrap the mean of each proportion and get in that way confidence interval of the mean. I observed 21 times a response that could be one out of 8 categories

Hello, everyone, There's a problem about zero in R and I really need your help. I have a vector shown as x=c(0.1819711,0.4811463,0.1935151,0.1433675), The sum of this vector is shown as 1 in R, but when I type 1-sum(x), the value is not zero, but -2.220446e-16. I can accept that this value is quite small and could be seen as zero, but there would be a problem when it's not really

I would like to estimate the sigma matrix of multinormal distribution through ML. But I don't know how to optimize the parameter sigma. Could any one help me? Thank you so much~ Yen Lee

Hi, I am trying to fit a model with an ordered response variable (3 levels) and 13 predictor variables. The sample has complex survey design and I've used 'svydesign' command from the survey package to specify the sampling design. After reading the manual of 'svyglm' command, I've found that you can fit a logistic regression (binary response variable) by specifying the

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)

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)

This is partly an R and partly a general statistics question. I'm trying to get confidence intervals of proportions (sometimes for subgroups) estimated from complex survey data. Because a function like prop.test() does not exist for the "survey" package I tried the following: 1) Define a survey object (PSU of clustered sample, population weights); 2) Use svyglm() of the package

Hello R-list, I'm attempting to migrate from Stata to R for my complex survey work. It has been straight-forward so far except for the following problem: I have some code below, but first I'll describe the problem. When I compute predicted logits from a logistic regression, the standard errors of the predicted logits are way off (but the predicted logits are fine). Furthermore, the

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 <-

I'm new to post in R-help and my native language is not English. I apologize if my sentence is not fluent to read. I am doing a simulation study and I need to execute SAS and read a SAS code in R. I try the following code but it doesn't work. system('"c:\\program files\\SAS\\SAS 9.1\\sas.exe" "c:\\syntax.sas"') can anyone give me some help with this?