similar to: Problem with package sem

Why does R CMD INSTALL work for some packages (e.g., lme4) but not others (e.g., nlme)? Thanks, Gang

I want to run deconvolution of a time series by an impulse or point-spread function through Wiener filter, regularized filter, Lucy-Richardson method, or any other approaches. I searched the CRAN website and the mailing list archive, but could not find any package for such a deconvolution analysis. Does anybody know an existing R function for deconvolution? TIA, Gang

I want to create a list based on the information from a data.frame, Model. So I tried the following: MyList <- list(colnames(Model)[2] = levels(Model$(colnames(Model)[2]))) but it failed with an error: Error: unexpected '=' in "list(colnames(Model)[2] =" I have the following problems with this command line: (1) I wanted to use colnames(Model)[2] as a tag for the list:

Suppose I have a 4-D array X with dimensions (dx, dy, dz, dp). I want to collapse the first 3 dimensions of X to make a 2-D array Y with dimensions (dx*dy*dz, dp). Instead of awkward looping, what is a good way to do this? Is there a similar function like reshape in Matlab? Thanks, Gang

For some unknown reason I stopped receiving any messages from the R- help mailing list. See if this test gets through. Thanks, Gang

This must be very simple, but I'm stuck. I have a command line in R defined as a variable of a string of characters. How can I convert the variable so that I can execute it in R? Really appreciate any help, Gang

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

Hello, I am using the SEM package in R to fit a multigroup latent variable model and ran into some difficulties. I have 2 questions: 1. First, I am getting the following error message and wondering what to do to fix it: Error in solve.default((N[g] - 1) * robustVcov(mod.g, adj.obj = adj.objects[[g]])) : system is computationally singular: reciprocal condition number = 4.52055e-23

Hi, we are trying to do structural equation modelling on R. However, one of our predictor variables is categorical (smoker/nonsmoker). Now, if we want to run the sem() command (from the sem library), we need to specify a covariance matrix (cov). However, Pearson's correlation does not work on the dichotomous variable, so instead we produced a covariance matrix using the Spearman's (or

Sorry if this question has been asked previously, I searched but found little. There also doesn't seem to be a dedicated SEM list-serv so hopefully this will find its way to the appropriate audience. In discussing SEM with a colleague I mentioned that a model they were fitting in AMOS was equivalent to a linear regression and that the coefficients would be the same. This of course was the

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 =

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

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

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,

Hello all, I have an issue where I am generating data and trying to confirm the estimates using a sem. I keep getting an error about the degree of freedom being negative "Error in sem.default(ram, S = S, N = N, raw = raw, data = data, pattern.number = pattern.number, : The model has negative degrees of freedom = -6" Can someone explain this error or tell me what is wrong with my

Hello I am using sem to look at the direct effect of one variable on another but i am uncertain if i am progressing correctly. An example: covar1<-? matrix(c(0.4,-0.2,3,-0.2 , 0.3,-2 , 3 ,-2 , 60), nrow=3,byrow=T) rownames(covar1)<-colnames(covar1)<-c("endo","exo","med") path1<-matrix(c(? ? "exo -> endo",? "g1", NA,

Dear All, I am new for R and SEM. I try to fit the model with Y (ordinal outcome), X (4 categorical data), M1-M3 (continuous), and 2 covariates (Age&sex) as a diagram. library(polycor) model.ly <-specify.model() 1: x -> m1, gam11, NA 2: x -> m2, gam12, NA 3: x -> m3, gam13, NA 4: age -> m1, gam14, NA 5: age -> m2, gam15, NA 6: age -> m3, gam16, NA 7: sex -> m1,

Hi all, I am attempting to learn my way through the sem package by constructing a simple structural model for some of my data on bird diversity, abundance, and primary productivity. I have constructed a covariance matrix between these variables as per the following: >S_matrix = matrix(c( >+ 0.003083259, 0, 0, >+ 0.143870284, 89.7648490, 0, >+ 0.276950919,

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

Hi. I have tried to use Package "SEM". As a learning, I try to convert a program running well of EQS which is as follows to SEM: ### EQS ### /SPECIFICATION CAS=100; VAR=5 MAT=COR; ANA=COR; /EQUATIONS V1=*F1+E1; V2=*F1+E2; V3=*F1+*F2+E3; V4=**F1+*F2*E4; V5=*F2+E5; /VAR E1 TO E5=*; F1*1.0; F2=1.0; /COV E1,E2=*; F1,F2=*: /PRINT FIT ALL; /MATRIX ...... /END This is the converted SEM