search for: underidentified

...in russian regions. I run the analysis by means of FIML in R sem package. I am not familiar with SEM yet, but I've just got several suitable estimated specifications. Nevertheless, sometimes R gives the following warning message: Warning message: Negative parameter variances. Model is probably underidentified. in: sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, I check for rank condition - all three equations in the system are turned out to be exact... Does anybody know what it means? and how to handle with that problem? P.S. Do you know any examples of models estimated...

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

...v.mat) model <- cfa(file = "http://dl.dropbox.com/u/1445171/cfa.model.txt", reference.indicators = FALSE) cfa.output <- sem(model, cov.mat, N = 900, maxiter = 80000, optimizer = optimizerOptim) Warning message:In eval(expr, envir, enclos) : Negative parameter variances.Model may be underidentified. Straight off you might notice a few anomalies, let me explain. - Why is the optimizer chosen to be optimizerOptim? ANS: I originally stuck with the default optimizerSem but no matter how many iterations I run, either I run out of memory first (8GB RAM setup) or it would report no convergence...

...umer demand MES = minimum efficient scale M = price/cost margin Gr = annual rate of growth of industrial production Dur = dummy variable for durable goods industry K = capital stock GD = measure of geographic dispersion of output Here, Equation (1) & (3) are just identified but equation (2) is underidentified My question is: How to carry out 2sls? l have read rhelp wrt to 2sls, example provided over there is summary(tsls(Q ~ P + D, ~ D + F + A, data=Kmenta)) # demand equation But I still do not have clear idea how to write 2SLS command in R wrt to the example l have mentioned .Can...

Can somebody help me with the following issue (SEM in R), please:   When I run the model (includes second order models) in R, it gives me the following:   1)       In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars,  :   Could not compute QR decomposition of Hessian. Optimization probably did not converge.   2)       I have aliased parameters and NaNS   or sometimes when

...df1,NA sem.RLIM=sem(model.RLIM,tcv,101) The output: Error in dimnames(x) <- dn : length of 'dimnames' [2] not equal to array extent In addition: Warning messages: 1: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : singular Hessian: model is probably underidentified. 2: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : refitting without aliased parameters. I use R version 2.10.0 (2009-10-26) under Windows XP sem_0.9-19 version. Where did I make a mistake? Have anyone of you knowledge of any other package doing similar thin...

Using R-studio, I am trying to run a structural equation model and I am running into problems with testing my primary model. Once I specify everything and try to run it I get this error: Error in eigen(S, symmetric = TRUE, only.values = TRUE) : 0 x 0 matrix And when I look at the object for my primary model in my workspace, which is created after I specify it, it lists all my model components,

...lt;- sof, lok2 rRU <- sof, lok3 sof <-> sof, lok4 cfa.ru <- sem(model.cfa.ru, data=ru) Error in w_mat %*% p_deriv_mat %*% invMat : requires numeric/complex matrix/vector arguments In addition: Warning message: In eval(expr, envir, enclos) : Negative parameter variances. Model may be underidentified. I've done a number of modifications to the model specification with no success. I'm wondering if I'm missing something? Thank you! [[alternative HTML version deleted]]

...g a hierarchical CFA using the sem package. I have 20 items, and I have 2 factors (F3 and F4), and also F1 and F2 are nested within F3. Here is the code that I have, but it is giving me an error message "Warning message: In eval(expr, envir, enclos) : Negative parameter variances. Model may be underidentified." and a further error "Error in summary.objectiveML(cfa, fit.indices = c("NNFI", "CFI", "RMSEA")) : coefficient covariances cannot be computed". I have run CFA before with no issues. This is the first time I am running a nested model. Any help will be gr...

Hi, I am using tsls function from sem package to estimate a model which includes large number of data. Among its predictors, it has a nominal data which has about 10 possible values. So I expand this parameter into 9-binary-value predictors with the coefficient of base value equals 0. I also have another continuous predictor. The problem is that, whenever I run the tsls, I will get 'System

Hi there, I searched R-help list with "path analysis" as keyword, and learn that sem package can do it. However, I don't figure out a way to construct the model for the path diagram as Fig. 1. in Huang et al. (2002)[1]. I try the following code: huang.cor <- readMoments(diag=FALSE, names=c('x1', 'x2', 'x3', 'y')) 0.76 0.91 0.72 0.94 0.77 0.83

...ace.model, S = grace.cov, N = 190) Error in solve.default(C) : ?Lapack routine dgesv: system is exactly singular In addition: Warning messages: 1: In log(det(C)) : NaNs produced 2: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, ?: ? singular Hessian: model is probably underidentified. (...) So far I've tried: 1. Fixing the variances of the latent variables 2. Allowing the exogenous indicators to covary (fixed.x parameter in sem()) 3. Manually inserting the published parameter estimates during model specification (specify.model()) to see if the starting parameters passed t...

...reply. > > Could you suggest how I can correct this problem? I tried using a > correlation matrix instead of raw moments, but still got the same > error. I also fixed parameters v1,v2,v3,a1 at 1; then it gave me the > error that the system is exatly singular. > The model is underidentified regardless of the input matrix. My point was that if you're using raw moments (as opposed to covariances or correlations) you should have a constant term in each equation. > To answer the points that you raised: > > 1. x1-x6 are not causes; they are just indicatiors. Does that change...

Full_Name: Charles J. Geyer Version: 1.8.0 OS: i686-pc-linux-gnu (Suse 8.2) Submission from: (NULL) (134.84.86.22) Two bugs (perhaps related, perhaps independent) revealed by the same Poisson regression with offset mydata <- read.table(url("http://www.stat.umn.edu/geyer/5931/mle/seeds.txt")) out.fubar <- glm(seedlings ~ burn01 + vegtype * burn02 + offset(log(totalseeds)),

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,

Hi - I am trying to fit sem model with intercepts. Here is what I have in my model. Exogeneous vars: x1 (continous), x2 (ordinal), x3 (ordinal), x4(continuous) Endogeneous vars: y1 (continuous), y2 (ordinal), y3 (ordinal) SEM model: x1 -> eta1; x2 -> eta1; x3 -> eta2; x4 -> eta2; eta1 -> y1, eta1 -> y2, eta2 -> y2, eta2 -> y3 However, in these arrow models, I

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,

...an.cs', 'ln.pop.gr.89.26mbe', 'ln.pass.railway.percap', 'ln.city', 'permafrost', 'ln.indoutput.fuel.p0.pc', 'ln.phone1995', '(Intercept)'), raw=TRUE) Then, R returns: Warning message: Negative parameter variances. Model is probably underidentified. in: sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Obtained summary: summary(model.3eq.33.estim) Model fit to raw moment matrix. Model Chisquare = 398.81 Df = 24 Pr(>Chisq) = 0 Goodness-of-fit index = 0.944 Adjusted goodness-of-fit index = 0....

I hope somebody can help. A student of mine is doing a study on Measurement Error models (errors-in-variables, total least squares, etc.). I have an old reference to a "multi archive" that contains leiv3: Programs for best line fitting with errors in both coordinates. (The date is October 1989, by B.D. Ripley et al.) I have done a search for something similar in R withour success. Has

I'm using specify.model for the sem package. I can't figure out how to represent the residual errors for the observed variables for a CFA model. (Once I get this working I need to add some further constraints.) Here is what I've tried: model.sa <- specify.model() F1 -> X1,l11, NA F1 -> X2,l21, NA F1 -> X3,l31, NA F1 -> X4,l41, NA F1 -> X5, NA, 0.20