similar to: saving datafreame object problem

Hi All I was wondering how to get the same results with gls and lme. In my lme, the design matrix for the random effects is (should be) a identity matrix and therefore G should add up with R to produce the R matrix that gls would report (V=ZGZ'+R). Added complexity is that I have 3 levels, so I have R, G and say H (V=WHW'+ZGZ'+R). The lme is giving me the correct results, I am

I was reading the help but just did not get how to specify starting values for varIdent() of the lme() function, although I managed to do it for corSymm(). Do I specify the values just as they are printed out in an output, like c(1, 1.3473, 1.0195). Or do I need to take the residual and multiply it with these like c(0.2235, 0.2235*1.3473, 0.2235*1.0195) or any other form that I dont know of?

Hi Reading the help for ?unz I was wondering if I can read data into R from within an zipfile that is on some website, like maybe: dtaa = read.table(unz("http://www.ats.ucla.edu/stat/examples/alsm/alsm.zip","Ch01pr19.dat")) Thanks for letting me know if you came acros such a thing before. Toby

Hi, I recently tried to estimate a linear unconditional latent growth curve on 7 repeated measures using lavaan (most recent version): modspec=' alpha =~ 1*read_g0 + 1*read_g1 + 1*read_g2 + 1*read_g3 + 1*read_g4 + 1*read_g5 + 1*read_g6 beta =~ 0*read_g0 + 1*read_g1 + 2*read_g2 + 3*read_g3 + 4*read_g4 + 5*read_g5 + 6*read_g6 ' gmod=lavaan(modspec, data=math, meanstructure=T,

Dear list, I want to run a statistical program (using its .exe file) from R by writing a script. I know there are some packages that call WinBUGS, Mplus etc. form R. I just want to call the .exe extension of this program and run several times writing a code in R. Thus, I want to have the output inside R. I just don't know where to start. Does anyone have any idea about that? Is there a

Is it possible to fit a structural equation model with link functions in R? I am trying to build a logistic-regression-like model in sem, because incorporating the dichotomous variables linearly seems inappropriate. Mplus can do something similar by specifying a 'link' parameter, but I would like to be able to do it in R, ofcourse. I have explored the 'sem' package from John Fox,

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, Recently, I was asked to help out with an SEM cross-validation analysis. Initially, the project was based on "sample-splitting" where half of cases were randomly assigned to a training sample and half to a testing sample. Attempts to replicate a model developed in the training sample using the testing sample were not entirely successful. A number of parameter estimates were

R list- i have begun using the "MplusAutomation" while piloting a large-scale simulation (~200,000 replications). since the package takes advantage of the DOS batch mode available in Mplus, each replication starts and activates a new instance of a command prompt window. this effectively locks me out of my computer for the duration of the simulation. my question is this: can anyone

Afternoon R help, I want to run Rasch/IRT analyses using the ltm package, however, I am using large scale survey data which requires weighting for accurate results. I attempted to create a weighted object to insert into the formulae of the ltm packages, however, the survey data only includes 30 replicate weights and a sampling weight. The svrepdesign requires additional information such as

Hello. Using the polychor function > polychor(data[c(s1,s2)] ) for polychoric correlations of two ordinal variables in R takes a long time for N=7000 (20 minutes+) and significantly slows down my computer. Now, I have a pretty old computer, but it takes about 20 seconds for MPLUS to print out the complete polychoric correlation matrix for all 16 variables, while I am running the R function

I was in a presentation of optimizations fitted with both MPlus and SAS yesterday. In a batch of 1000 bootstrap samples, between 300 and 400 of the estimations did not converge. The authors spoke as if this were the ordinary cost of doing business, and pointed to some publications in which the nonconvergence rate was as high or higher. I just don't believe that's right, and if some

> 3x4 Error: unexpected symbol in "3x4" R has no idea that you equate "x" as multiplication.. use an astrix > 3*4 [1] 12 dominic wrote > > This is basically my code: > > library(MASS) > lmsreg(formula = b0 ~ b1 + b3 + b1xb2, data=mydata) > > b1xb2 is an interaction but it was the centered value for a continuous > variable times a

Hello, I've been trying to answer a problem I have had for some months now and came across multivariate multilevel modeling. I know MPLUS and SPSS quite well but these programs could not solve this specific difficulty. My problem: 9 correlated dependent variables (medical symptoms; categorical, 0-3), 5 measurement points, 10 time-varying covariates (life events; dichotomous, 0-1), N ~ 900.

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Hi All I dont know if anyone else has noticed the same thing, but with 2 subsequent runs of the same syntax, I am getting exactly the same results. I was expecting that results differ slighlty, say in the 4th or 5th decimal place. Is this a specialty with R2WinBUGS? Does it have something to do with the seed value? Isnt the seed value reset everytime I restart winbugs? Thanks Toby

I have been working through the book "Applied longitudinal data analysis: modeling change and event occurrence" by Judith D. Singer and John B. Willett. I have been working examples using SAS and also using it as an opportunity for learning to use R for statistical analysis. I ran into some difficulties in chapter 8 which deals with using structural equation modeling. I have tried to

I am looking to fit one or more latent categorical variables to data that is a mixture of categorical and continuous variables. Factor analysis would work for continuous data, latent class analysis for categorical data. I understand that in a package such as MPlus I could perform a single analysis of both data types. Are there similar routines available in R? Stuart -----Original Message-----

Hi All I am pretty new to R but saw stata and sas's macro facilities and am looking for how such things work in R. I am trying to piece together a series of statements: n = 5 #want to have it dynamic with respect to n for (j in 1:n) { eval(paste("x", j, "=x[", j, "]", sep="")) } I want the created statements 'x1=x[1]' immediately executed

Has anyone successfully linked Mplus and R and have code on how to do it? It would be great to use R to simulate datasets and then ship to Mplus for SEM analysis. I know one researcher who does this quite regularly but I can’t get ahold of him right now. Sincerely, Jeff Miller **************************************** Jeffrey M. Miller, PhD HYPERLINK