similar to: bootstrapping: multilevel and multiple mediation

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 +

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

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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

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, 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, I have done a scatterplot and now would like to add its regression line but it does not show. Below, the code I have used. lm3 <- lm(data$S_pH_KCl2.5_BCx~data$B_OleicoPF_BCx_per) plot(data$S_pH_KCl2.5_BCx, data$B_OleicoPF_BCx_per) abline(lm3) I have been able to do the complete operation using the software STATISTICA but it would be great to do it with R. If you require more details

Hello, I would like to perform a Box-Cox (“bcPower”) transformation on my data. For this, I am determining lambda using the “powerTransform” function. However, with one of my variables I get the following Warning Message: In estimateTransform(x, y, NULL, ...) : Convergence failure: return code = 52 My variable is: > x [1] 0.0001031130 0.0001029480 0.0001040010 0.0001037940 0.0001046280

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()

Dear all, I have just sent an enquiry but probably I hadn’t expressed myself properly. Could anyone help me with the following? When I run the code on my data I get a boxplot with outliers identified by numbers 200 & 201. However, what I would like is to label these outliers with their corresponding “DATA$num” values of the data frame. In this example, the outliers should be labelled as:

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

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

Dear all, Could anyone help me with the following? DATA <- data.frame(rbind(c("Red1", 1, 1, 1), c("Blue1", 1, 1, 1), c("Red2", 1, 1, 1), c("Red3", 1, 1, 1))) colnames(DATA) <- c("A", "B","C", "D") #Option 1 DATA <- DATA[-2, ] #Same result I would like to achieve with Option 2 #Option 2 - I would like to do

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

From: aguitatierra@hotmail.com Sent: Friday, April 05, 2013 11:47 PM To: r-help@r-project.org ; R Help Subject: Reversing data transformation Hi everybody, I would be very grateful if you could give me your thoughts on the following issue. I need to perform Box-Cox (bcPower€) transformation on my data. To do this, I calculated lambda using the function '€powerTransform'€.

Hi everybody, I would be very grateful if you could give me your thoughts on the following issue. I need to perform Box-Cox (bcPower€) transformation on my data. To do this, I calculated lambda using the function '€powerTransform'€. powerTransform(data) However, I got an error message when performing this function: Convergence failure: return code = 52 I was told by John Fox

Dear R team / R helpers / R users , I downloaded R-1.0.0 to install it but I am currently facing the following problem : Installation of packages. Here is the output I had while trying to install RmSQL : ------------- > R INSTALL -l /software/R-1.0.0/library/ /software/archive/R/RmSQL_0.2-1.tar.gz Installing package `RmSQL' ... libs RmSQL.c:7: msql.h: No such file or directory make:

I am using a multilevel modeling approach to model change in a person's symptom score over time (i.e., longitudinal individual growth models). I have been using the lme function in the multilevel package for the analyses, but my problem is that my outcome (symptoms) and one of my predictors (events) are count data, and are non-normal. Do you have any suggestions for how to deal with them?

Hi, does anyone out there have a recommendation for multilevel / random effects and longitudinal analysis? My dream book would be something that's both accessible to a non-statistician but rigorous (because I seem to be slowly turning into a statistician) and ideally would use R. Peter

It is not a pure R question,but I hope some one can give me advices. I want to use analysis my data with the multilevel model.The data has 2 levels---- the second level has 52 units and each second level unit has 19-23 units.I think the sample size is quite small,but just now I can't make the sample size much bigger.So I want to ask if I use the multilevel model to analysis the data set,will