similar to: SEM - standardized path coefficients?

Dear list, I have a data file which includes, alongside various variables representing questionnaire scores, a variable for survey weights computed as the number of observations in the sample drawn from that group divided by the number of observations in the population in the group. I need to calculate a covariance matrix of the questionnaire scores for use in sem. How do I apply the weights?

Hi list members Any easy way to get standardized cronbach's alpha for a scale, as in SPSS? Thanks Steve Powell proMENTE social research research | evaluation | training & consulting Kranj?evi?eva 35, 71000 Sarajevo mobile: +387 61 215 997 | office: +387 33 556 865 | fax: +387 33 556 866 Checked by AVG Free Edition. 17:44

Hi, Is it possible to assign a separate colour for comments written with #, eg:- #this is a comment . I am looking to colour them differently from the program text in R-Editor (not console). Is it possible to do so? Eg. In Visual basic, the colour for remarks gets green automatically Regards Sumit [[alternative HTML version deleted]]

Dear expeRts, sorry for such a newbie question - in PCA/factor analysis e.g. in SPSS it is possible to save scores from the factors. Is it analogously possible to "save" the implied scores from the latent variables in a measurement model or structural model e.g. using the sem or lavaan packages, to use in further analyses? Best wishes Steve Powell www.promente.org | skype

Dear list members, I am trying, within a lapply command, to print the name of the objects in list or data frame. This is so that I can use odfWeave to print out a report with a section for each object, including the object names. I tried e.g. a=b=c=1:5 lis=data.frame(a,b,c) lapply( lis, function (z) { obj.nam <- deparse(substitute(z)) cat("some other text",obj.nam,"and so

hi list members I am trying to use odfWeave with R 2.5.1 on Windows XP. however when running e.g. odfWeave(demoFile, outputFile) I get: Error in odfWeave(demoFile, outputFile) : Error unzipping file In addition: Warning message: unzip not found in: system(zipCmd[2], invisible = TRUE) presumably my zip and unzip are not set up correctly but I dont know how to do that. I installed zip and

Dear list members I would like to apply a vector of labels v=c("lab1","lab2","lab3") to a dataframe df=data.frame(1:3,1:3,1:3) using some kind of loop or apply function. Any ideas? Thanks Steve Powell Checked by AVG Free Edition. 17:03 [[alternative HTML version deleted]]

Hello - Does anybody know how to get the SEM package in R to return standardized path coefficients instead of unstandardized ones? Does this involve changing the covariance matrix, or is there an argument in the SEM itself that can be changed? Thank you, Tim [[alternative HTML version deleted]]

I'm not aware of any routine that those the job, although I think that it could be relatively easily done by multiplication the manifest variable vector with the estimates for the specific effect. To make an example: v1; v2; v3; v4 are manifest variables that loads on one y latent variablein a data frame called "A" the code for the model should be like: model <-specifymodel( y

Hi everyone, I just conducted a structural equation model for estimating a response model. This model should predict the probability that someone is responding to a direct mailing. I used the sem package for this. When I have my coefficients I want to know how well my model predicts the probability of response. How can I calculate these probabilities? I tried to use the unstandardized

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

Dear list members I am still a newbie so might be asking a stupid question, but I can't get ggplot to work in a loop (or a "while" statement for that matter). # to take a minimal example - mydata$varc = c(1,2,3) for (i in 1:1){ jpeg("test3.jpg") plot(mydata$varc) #ggplot(mydata, aes(x=mydata$varc)) + geom_bar() dev.off() } this produces

hi, I plot path diagrams with the path.diagram() function of the sem package in combination with the graphviz application. Now I want the graphviz code for a path-plot with the actual standardized coefficients on the arrows (not the names). I tried to add edge.labels="values" as an argument to path.diagram() but it's just ignored. can anyone help me on that? p.s.;

Hello, I have conducted an SEM in which the resultant standardized path coefficients are much higher than would be expected from the raw correlation matrix. To explore further, I stripped the model down to a simple bivariate relationship between two variables (NDVI, and species richness), where it's my understanding that the SEM's standardized path coefficient should equal the correlation

Hello. I am getting results from sem that are not correct (that's assuming that the results from my AMOS 4.0 software are correct). sem does not vary some of the parameters substantially from their starting values, and the final estimates of those parameters as well as the model chisquare value are incorrect. I've attached some code that replicates the problem. The parameters in

Dear wizaRds, I have a dataset to analyse which is causing me problems. It is a sample of parents in schools. First we had a population table of the schools in the country in question divided into five regions, and in each region we have an urban/rural split. The population Ns in these ten cells are known. Then three schools were drawn from each cell according to the Lahirie method, i.e with

Hello, I finally run my measurement model in sem - successfully. Now, I am trying to print out the path diagram that is based on the results - but for some reason it's not working. Below is my script - but the problem is probably in my very last line: # ANALYSIS OF ANXIETY, DEPRESSION, AND FEAR - LISREL P.31 library(sem) # Creating the ANXIETY, DEPRESSION, AND FEAR intercorrelation matrix

I'm using specify.model for the sem package. I can't figure out how to represent the residual errors for the observed variables for a CFA model. (Once I get this working I need to add some further constraints.) Here is what I've tried: model.sa <- specify.model() F1 -> X1,l11, NA F1 -> X2,l21, NA F1 -> X3,l31, NA F1 -> X4,l41, NA F1 -> X5, NA, 0.20

Hi Has anyone used R to conduct confirmatory factor analysis? This email pertains to use of SEM. For context consider an example: the basic idea is that there are a bunch of observables variables (say study habbits, amount of time reading in the bus, doing homework, helping other do homework, doing follow-up on errors etc.) and one believes that all these variables maybe measured by two or

I run 9 WLS regressions in R, with 7 predictors each. What I want to do now is compare: (1) The strength of predictors within each model (assuming all predictors are significant). That is, I want to say whether x1 is stronger than x2, and also say whether it is significantly stronger. I compare strength by simply comparing standardized beta weights, correct? How do I compare if one predictor is