similar to: help with mediate() and medsens()

Hello all, I apologize for the simplistic question, but I have been having some trouble learning how to do mediation analysis in R. Ideally, I would like to use Preacher's Bootstrapping test for mediation (Preacher & Hayes, 2004). I have attempted to use the mediate package to set this up, using code that looks basically like this: model.m <- lm(data$outcome ~ data$mediator +

Hello! I have problem with mediation analysis. I can do it with function mediate, when I have one mediator. But how I can do it if I have one independent variable and one dependent variable but 4 mediators? I have try function mediations, but it dosen't work. If I use mediate 4 times, each for every mediator, is it same? I want to know what is total mediate effect for 4 mediators. t.Mete

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Dear friends, i've a little problem with a study about mediation. I will call my dependent variable "Y", the independent "X" and "Z" will be my mediator variable... Using a linear regression, i can say that there isn't a direct effect of X on Y because the coefficient of X isn't significative, but i've tried to use anyway the Sobel's test to

Hi everyone,I would like to test a mediation model, that has several control variables. More specifically, I would like to test the indirect effect with bootstrapping. However, all the packages I have found so far (e.g. MBESS) only allow testing a simple mediation model (One independent, one mediator, one dependent) so that I cannot include any controls. Can somebody help me? [[alternative HTML

Hello, Have someone performed a bootstrap in a multiple-mediator model? I am trying to compute a bootstrap in a multiple and multilevel mediation. Up top now, I have developed bootstraps in random coeffient models, but I am very lost concerning the mediation. Could someone to provide me some ideas about syntax in R? Thank you very much in advance, Bea

Hi everyone, I use the boot package within the MBESS package to automate much of the hard part for folks interested in performing a (simple) mediation model. The function mediation() works well, except for a (probably) unrealistic artificial data set. When I apply the bootstrap I sometimes get: Error in t.star[r, ] <- res[[r]] which is (I think) due to Inf and -Inf and NaN values produced for

Hello, In a study exploring transgenerational transmission of anxiety disorder we investigate whether infants react to experimentally induced mood changes of their mothers. We measured the time that an infant needed to cross a cliff (=crossing time) depending on whether his mother had previously undergone a mood induction (treatment) or not (control). The treatment is thus a

Package update: mediation Scholars across a range of disciplines are increasingly interested in identifying causal mechanisms, going beyond the estimation of causal effects. The mediation package (4.0) estimates mediation effects for a variety of experimental designs and statistical models. The update covers new models, designs, and includes many new functionalities. Dustin Tingley Government

FYI, for those of you who aren't on the serusers list. I'd like to hear how others can get this working in small Asterisk settings; I don't really have the time to implement it, but it looks very interesting. JT >To: serusers@iptel.org >From: Adrian Georgescu <ag@ag-projects.com> >Date: Mon, 31 May 2004 23:05:47 +0200 >Subject: [Serusers] CDR mediation for VoIP

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

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

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

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

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

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

Hello, As obtained from a table svyglm clasificaion, sensitivity and specificity. The funtion ConfusionMatrix () of the library (caret) gives these results but not how to apply it to svyglm. thanks [[alternative HTML version deleted]]

Does anyone know how to calculated a BIC score (or an equivalent model fitness score) when using svyglm for logistic regressions? Thanks Brad -- View this message in context: http://r.789695.n4.nabble.com/Generating-a-model-fitness-when-score-using-svyglm-tp2069280p2069280.html Sent from the R help mailing list archive at Nabble.com.

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)

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