similar to: R SEM package

Hello- If I input a variance-covariance matrix and specify NA for start values, how does sem determine the start value? Is there a default? Anthony -- Anthony Steven Dick, Ph.D. Post-Doctoral Fellow Human Neuroscience Laboratory Department of Neurology The University of Chicago 5841 S. Maryland Ave. MC-2030 Chicago, IL 60637 Phone: (773)-834-7770 Email: adick at uchicago.edu Web:

Hello all, This is a general stats question--I realize it is an R help list, so tell me to go away if it is inappropriate. I have a 2 X 2 design, and I have specified four identical path models (one for each level of each factor). I want to test for an interaction at each path--essentially (A1 - A2) - (B1 - B2) != 0. I was thinking of computing a contrast for each path of interest, such that

Hello- I am using R to build my initial models, but need to use AMOS to compare the models of two groups (adults vs. kids). The problem is I am getting different results with R and AMOS for the initial models of the separate groups (and the R results make more sense). The parameter estimates (path coefficients and variances) from both programs are nearly identical, but the model chi-squares

Hello- I would like to remove the columns of a matrix that contain all zeros. For example, from x<-matrix(c(1,5,3,2,1,4,0,0,0), ncol=3,nrow=3) I would like to remove the third column. However, because this is in a loop I need a way to first determine which columns are all zeros, and only then remove them. I.e., I don't know which column of x contains all zeros until after x is

Hi all- I am stumped. The code in A. returns errors at lines 14 and 15 and fails to load series1 and series2. However, in B., if temp1 and temp2 are called again (which returns a "Table exists" error; see lines 14-17 in B.) series1 and series2 load correctly. Any ideas? Also-I am open to any suggestions to improve the code as I am a horrific programmer. Thanks A. 1 >

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

Hello- I have a script which steps through a series of subjects, and for the subjects I remove outlying values. After removing these outliers, I specify a cutoff, keeping only values over a certain value (e.g., 1.96). I want to populate a matrix with a statistic of the values that make the cutoff (for example, the mean). However, in some subjects, after outliers and the cutoff are specified,

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

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

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 am using R and LISREL for simulation studies. R generates the data that is analyzed with LISREL. In LISREL I use "PV" in the LISREL output statement to request estimated variances. LISREL writes these in a file that looks like this: 1 0 0 0.100331D+01 0.144845D+01 0.141009D+01 0.214423D+01 0.214129D+01 0.194464D+01 0.191531D+01 0.198328D+01 0.100683D+00-0.236392D-01

Dears, I'm a college student and In doing my statistics homework. I use R with SEM package as my tool for sem analysis, but my teacher told me AMOS is more suitable for such analysis. Could someone help tell me whether it is true that some commercial software is better accepted in academic fields? Sorry if I should not post such topics here. -- Best Regards, Reeyarn T. Lee Accounting

A bit late, but you might like to look at http://www.qimr.edu.au/davidD/polyr.R Regarding the original posters queries: You can analyse polychoric correlations as if they were Pearson correlations using standard software (eg sem), and this usually doesn't do too badly, or go to AWLS (Browne) in LISREL etc, or ML analysis of the full multidimensional contingency table using programs such as

Hello, I have access to my database via command line and through workbench, and have access on the grant tables: mysql> SELECT host,user,password,select_priv,insert_priv FROM user; +------+-------+-------------------------------------------+-------------+-------------+ | host | user | password | select_priv | insert_priv |

Hi, (1.) How is it possible to get automatic a "lower triangle of correlation matrix" ? h.cor <- cor(dat,use="pairwise.complete.obs") zz <- lower.tri(h.cor,diag=T) ### that's not what i wish and "wrong" ? results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)])) results <- matrix(unlist(h.cor[upper.tri(h.cor,diag=T)]),5) Must i take the lowest

People of R(th), I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to parallelize a bootstrap of a linear model on my 8-core mac. Below is the process that I want to parallelize (namely, the m2.ph.rlm.boot<-boot(m2.ph,m2.ph.fun, R = nboot) command). This is an extension of the bootstrapping linear models example in Venables and Ripley to

I don't know of any R package that can match all the functionality of LEM eg fitting equality constraints to model parameters a la LISREL. WRT dumping tables, I would have thought that as.data.frame.table does pretty much what you want, [not tested] newtab <- as.data.frame(table(a,b,c)) cat("dim\n") for(i in seq(1, ncol(newtab)-1) { cat(nlevels(newtab[,1]," ") }

Hi there, I just saw your question on the help list and tough you could help me with some start-up info.... I just downloaded the R project software to try running SEM analysis and I also downloaded the SEM package. But it just sits there on the desktop and R does not know where it is...I can¹t figure out how to organize the files...When I type ?sem in R it says object sem not found...I can¹t

Hi list, I'm performing a series of confirmatory factor analysis on different groupings of items from data collected with questionnaires. There are some missing values. For those sets with no missing values I call factanal(datamatrix,factors=n) where datamatrix is a table of all observations for the items under investigation. This call fails when there are missing values. help(factanal)

Hi there, I just saw your question on the help list and tough you could help me with some start-up info.... I just downloaded the R project software to try running SEM analysis and I also downloaded the SEM package. But it just sits there on the desktop and R does not know where it is...I can¹t figure out how to organize the files...When I type ?sem in R it says object sem not found...I can¹t