similar to: two new packages

Hello. I want to get the proper influence function for the glm coefficients in R. This is supposed to be inv(information)*(y-yhat)*x. So I am wondering what is the exact mathematical formula for the output that the functions: dfbeta() OR lm.influence()$coefficients return for a glm model. I am confused because: 1. Their columns don't sum to zero as influences should. 2. They

Dear R list members, I have a question about the proper use of the typsize and fscale arguments to nlm. I use nlm in my sem package to fit general structural-equation models, which entails maximizing a multinormal likelihood with respect to parameters that represent regression coefficients and covariances of variables. The magnitudes of these parameters can be very different. The

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 am wondering whether Package SEM can do with intercepts and means in its structural analysis. If it can not calculate, how can I make a supplemental function in R? Many thanks in advance. --------========---------- Mitsuo Igarashi mitsu5 at ruby.famille.ne.jp

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

Dear Helplist, I want to produce forecasts from a structural equation model. With the SEM package the model setup and its estimation is possible. However, I have not figured out how to obtain ex ante forecasts, i.e. applying the Gauss-Seidel algorithm to the estimated structural equations for provided values of the exogenous variables (i.e.: y_t = -inv(A)*B*x_t). Does anyone know if the there is

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,

I have just started using the SEM package in R, so I'm not sure I'm doing everything right, but I keep getting an error concerning startvalues and I can't figure out how to fix it. Is anyone willing to read over my code and help me out??? Please email me if you are willing to look at my code and I will send it to you. Thank you so much, and Happy New Year! cathy

I am new to R and would like to get some practice analyzing multilevel data. I wonder if anyone can point me to a sample data set and command lines that I might replicate for a sample session. I would then compare my output with HLM output. Any help is appreciated. ------ Harold C. Doran Director of Research and Evaluation New American Schools 675 N. Washington Street, Suite 220 Alexandria,

Dear R list members, Since today seems to be the day for optimization questions, I have one that has been puzzling me: I've been doing some work on sem, my structural-equation modelling package. The models that the sem function in this package fits are essentially parametrizations of the multinormal distribution. The function uses optim and nlm sequentially to maximize a multinormal

Hi, I am trying to run a structural equation model in the sem package and i have debug set to true, but i get an error that says "subscript out of bounds" with no further explanation. Is there anymore documentation available for this or is there any generic reason to what this might refer ? Thanks, Adam _________________________________________________________ ____________ Adam

Dear R-help list members, I'm writing a package using tcltk. Thanks to Peter Dalgaard's excellent work on the tcltk package, almost everything has gone very smoothly. I'm stymied, however, by the following problem: I want to incorporate a bitmap image, stored as an xbm file, in a widget. To take a simple example, top <- tktoplevel() tkgrid(tklabel(top,

R Users I am a new user of R. I have sample weights that I would like to apply to some of the variables in my data set. Where can I go for information on how to do that? Richard

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

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

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

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

Dear r-devel list members, Dirk Eddelbuettel brought the following problem to my attention. The code is abstracted from the appendix on mixed models from my R and S-PLUS Companion: > set.seed(12345) # for reproducibility > library(nlme) Loading required package: lattice > data(MathAchieve) > data(MathAchSchool) > attach(MathAchieve) > mses <- tapply(SES, School,

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 R-Helpers: What other references are there on the capabilities of the survival5 package other than the help files and the chapter on survival analysis in every edition of Modern Applied Statistics with S? I'm thinking of something like "An Introduction to Survival Analysis in R" with worked examples that might complement or extend the chapter in MASS. Thanks, Spencer