similar to: "save scores" from sem

Dear list members, In sem, std.coef() will give me standardized coefficients from a sem model. But is there a trick so that path.diagram can use these coefficients rather than unstandardized ones? Thanks Steve Powell From: John Fox <jfox_at_mcmaster.ca> Date: Wed 28 Feb 2007 - 14:37:22 GMT Dear Tim, See ?standardized.coefficients (after loading the sem package). Regards, John John

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?

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]]

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

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 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 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

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

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

I am a new user of the function sem in package sem and lavaan for structural equation modeling 1. I don?t know what is the difference between this function and CFA function, I know that cfa for confirmatory analysis but I don?t know what is the difference between confirmatory analysis and structural equation modeling in the package lavaan. 2. I have data that I want to analyse but I have some

I have been trying to use lavaan (version 0.4-7) for a simple path model, but the program seems to be computing far less degrees of freedom for my model then it should have. I have 7 variables, which should give (7)(8)/2 = 28 covariances, and hence 28 DF. The model seems to only think I have 13 DF. The code to reproduce the problem is below. Have I done something wrong, or is this something I

Hello all I'm trying to run some path analysis in either sem or lavaan (preferably lavaan because I find its interface easier to use). Most of my variables are continuously distributed and fairly well-behaved but I have a single exogenous variable (sex) which is not continuously distributed. Preliminary model fitting suggests that there aren't any sex by (anything else) interactions. The

Hi Sorry for the rather long message. I am trying to use the cfa command in the lavaan package to run a CFA however I am unsure over a couple of issues. I have @25 dichotomous variables, 300 observations and an EFA on a training dataset suggests a 3 factor model. After defining the model I use the command fit.dat <- cfa(model.1, data=my.dat, std.lv = T, estimator="WLSMV",

Is there a way to use full information maximum likelihood (FIML) to estimate missing data in the sem package? For example, suppose I have a dataset with complete information on X1-X3, but missing data (MAR) on X4. Is there a way to use FIML in this case? I know lavaan and openmx allow you to do it, but I couldn't find anything in the documentation for the sem package. Thanks! -- Dustin Fife

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I am sure there is a simple way to do the following, but i haven't been able to find it. I am hoping a merciful soul on R-help could point me in the right direction. I am doing a factor analysis on survey data with missing values. to do this, I run: FA1<-factanal(na.omit(DATA), factors = X, rotation = 'oblimin', scores = 'regression') Now that I have my factors and

Hi, I wonder why factor scores produced by factanal are correlated, and I'd appreciate any hints from people that may help me to get a deeper understanding why that's the case. By the way: I'm a psychologist used to SPSS, so that question my sound a little silly to your ears. Here's my minimal example: *********************************************** v1 <-

Dear all, i wonder if there?s a command to obtain the actual values of a principal component or a factor (not as.factor, but factanal) . test=princomp(USArrests, cor = TRUE) summary(test) just outputs, standard deviation, Prop of Variance and cumulative proportion of variance. test$loadings offers yet another proportion of variance scheme. why is that? Apart from that: Is there a

Hi! I am trying to analyse with R a database that I have previously analysed with SPSS. Steps with SPSS: Factorial analysis Extraction options : I select = Principal component analysis Rotation: varimax Steps with R: I have tried it with varimax function with factanal or with princomp...and the results are different of what I have with SPSS. I think that varimax function is incorporated in

Any advice on which package I can use for calculating effect sizes for two dependent samples? compute.es seems only to consider independent samples. Thanks in advance Steve Powell [[alternative HTML version deleted]]