similar to: Fwd: mediation analysis with R

Hello R users! I've been experimenting with lmer to estimate a mixed model with a dichotomous dependent variable. The goal is to fit a hierarchical model in which we compare the effect of individual and city-level variables. I've run up against a conceptual problem that I expect one of you can clear up for me. The question is about random effects in the context of a model fit with a

If I use latex(summary(X)) where X is a data frame with four variables I get something like Rainfall Education Popden Nonwhite Min. :10.00 Min. : 9.00 Min. :1441 Min. : 0.80 1st Qu.:32.75 1st Qu.:10.40 1st Qu.:3104 1st Qu.: 4.95 Median :38.00 Median :11.05 Median :3567 Median :10.40 Mean :37.37 Mean :10.97 Mean :3866

Dear colleagues, working with the data frame below, trying to reverse two variables I the error message below. i searched through the help list but could not find any postings which could help me solve the situation. I tried attaching and detaching the data frame to no avail. Yours, Simon Kiss *DATA FRAME 'data.frame': 1569 obs. of 9 variables: $ equal : num 3 4 3 2 3 4 2 3 2 2 ...

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 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

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

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 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

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

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 +

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, 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

Hello all, I am running Samba 2.2.8a and am trying to connect to a Windows 2003 domain controller server1 (BDC ? back up). I keep getting the error "Tree connect failed - NT_Status_Access_Denied" I have another domain controller server2 (PDC - primary)in the same domain with the same share etc and I can connect successfully. The only difference is the version of WIN2003 R2 for

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Dear users, In my psychometric test i have applied logistic regression on my data. My data consists of 50 predictors (22 continuous and 28 categorical) plus a binary response. Using glm(), stepAIC() i didn't get satisfactory result as misclassification rate is too high. I think categorical variables are responsible for this debacle. Some of them have more than 6 level (one has 10 level).

I'm having trouble with the mediate() and medsens() functions from the mediation package. My treatment, mediator and outcomes variables are all continuous and scaled to the 0-1 interval. The data is observational not experimental. I am thus using lm() for the initial outcome and mediation regression models. I also use weights as the data are survey data. (As a footnote, I use the svyglm()

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

Hi all, After setting permissions on a file. If anyone authorised to open the file writes to it, the ACL gets deleted and that person becomes the owner of the file and the group is reset to the default. For example, whenever a certain user opens and changes a file in the problem folder he becomes the owner and another gets kicked of the list: More clearly, lee was the last one to write to this

Hello all, this is the first time I'm using R and e1071 package and SVM multiclass (and I'm not a statistician)! I'm very confused, then. The goal is: I have a sentence with sunny; it will be classified as "yes" sentence; I have a sentence with cloud, it will be classified as "maybe"; I have a sentence with rainy il will be classified as "no". The