similar to: Coding design with repeated measurements in the survey package

Dear Professor Lumley I am relatively new to using R and also to logistic regression. We have analysed our Dudley Health Survey using the survey package. I am now trying to look at associations using svyglm but I am unsure of how to interpret the output and present the resulting model or whether there are any other things I should do to check the validity of the model. Below is an example of

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)

summary(result) Call: svyglm(Injury ~ seat, sD, family = quasibinomial(link = "logit")) Survey design: svydesign(~1, prob = NULL, strata = Data[, 1], weights = Data[, 4], data = Data, fpc = ~fPc) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -4.256875 0.001421 -2996.7 <2e-16 *** seatbad 0.681504 0.001689 403.4 <2e-16 *** ---

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 <-

Hello I want to know if the summary of the logistic model with survey Pr (> | t |) to test if the coefficient of the model is significant, ie is the p_valor wald test for the model coefficients, for I am interested to know if the three levels of the variable educational level are significant to the model (significance of handling 0.2), I present below the results of my model

Dear all, We are analysing some survey data and we are not sure if we are using the correct syntax for our design. The population of interest is a set of 4416 polygons with different sizes ranging from 0.003 to 45.6 ha, 7460 ha in total. Each polygon has a binary attribute (presence/absence) and we want to estimate the probability of presence in the population. We used sampling with replacement

Hi there I'm trying to fit a logistic regression model to data that looks very similar to the data in the sample below. I don't understand why I'm getting this error; none of the data are proportional and the weights are numeric values. Should I be concerned about the warning about non-integer successes in my binomial glm? If I should be, how do I go about addressing it? I'm

Hello, I'm trying to use the function svyglm in the library survey. I create a data survey object: data_svy<- svydesign(id=~PSU, strata=~sample_domain, weights=~sample_weight, data=data, nest=TRUE) and I try to use svyglm() with little success: R<-svyglm(data_svy[,4]~(data_svy[,iCol]==listModality[[iVar]] [iMod]),design=data_svy, family=binomial(link="logit") Error in

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

I have this problem. test <- svydesign(id=~1,weights=~peso) logit <- svyglm(bach ~ job2 + mujer + egp4 + programa + delay + mdeo + str + evprivate, family=binomial,design=test) then appear: Error in svyglm.survey.design(bach ~ job2 + mujer + egp4 + programa + : all variables must be in design= argument I don't know what this mean... Please help. Pablo. [[alternative HTML

Hi, I would like to estimate coefficients using poisson regression and then get standard errors that are adjusted for heteroskedasticity, using a complex sample survey data. Then I will calculate prevalence ratio and confidence intervals. Can sandwich estimator of variance be used when observations aren?t independent? In my case, observations are independent across groups (clusters), but

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

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 trying to run analyzes incorporating sample weight, strata and cluster (three-stage sample) with PNS data (national health survey) and is giving error. I describe below the commands used. I could not make the code reproducible properly. Thanks, ################################################# library(survey)####change to 0 and 1 variable

Hello, I want to specify a linear regression model in which the metric outcome is predicted by two factors and their interaction. glm() computes effects for each factor level and the levels of the interaction. In the case of singularities glm() displays "NA" for the corresponding coefficients. However, svyglm() aborts with an error message. Is there a possibility that svyglm()

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)

Hello, I am trying to call a FORTRAN subroutine from R. The Fortran code is @: http://lib.stat.cmu.edu/apstat/206 It performs a bivariate isotonic regression on a rectangular grid (m X n) matrix. I used the g77 compiler and successfully created a dll file and it also loads successfully from R. But somehow the programs fails to run properly. (I do get the correct result when I compile the

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

Gracias por la ayuda Jose Luis. pero o no te he entendido bien o mi duda es tan sencilla que no me he explicado. SI yo tampoco he entendido mal tu explicación, mi problema es cómo obtengo ese "tus.pesos" para introducir, por ejemplo, en la función: library(survey) # objeto del diseño muestral ddatos <- svydesign(id=~1, weights =~ tus.pesos, data = tus.datos) # en caso de una reg

Hi There: I was wondering if there is a way to read FORTRAN list data (similar to IDL's readf function). I often use FORTRAN for most of my number crunching, and use something like IDL to visualize and perform statistical analysis on that data. Since the each file is rather large (>100 Mb), formatting the output into columns or tables is impractical, hence the "list"