similar to: CFA in R/sem package

Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN

Hi, I would like to have an index for a column in a matrix encoded in a cell of the same matrix. For example: x = matrix(c(11,12,13,1, 21,22,23,3, 31,32,33,2),byrow=T,ncol=4) In this case, column 4 is the index. I then access the column specified in the index by: > for (i in 1:3) print(x[i,x[i,4]]) [1] 11 [1] 23 [1] 32 > > for (i in 1:3) {x[i,x[i,4]] <- x[i,x[i,4]] + 5} > x

I would like to buy a basic statistics book (experimental design, sampling, ANOVA, regression, etc.) with examples in R. Or download it in PDF or html format. I went to the CRAN contributed documentation, but there were only R textbooks, that is, textbooks where R is the focus, not the statistics. And I would like to find the opposite. Other text I am trying to find is multivariate data

Hi Sorry for the rather long message. I am trying to use the cfa command in the lavaan package to run a CFA however I am unsure over a couple of issues. I have @25 dichotomous variables, 300 observations and an EFA on a training dataset suggests a 3 factor model. After defining the model I use the command fit.dat <- cfa(model.1, data=my.dat, std.lv = T, estimator="WLSMV",

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

Hi, I want to run t.test() for several variables among two groups, and I would like to skip the tedious process of collecting information to assemble a table, but I am not sure if the function I want already exists. Any suggestion would be appreciated. I have a working example, as required by the posting guide: my_swiss = swiss[-1,] my_swiss$facto = rep(1:2,nrow(my_swiss)/2)

Hi, Why sum() on a 10-item vector produces a different value than its counterpart on a 2-item vector? I understand the problems related to the arithmetic precision in storing decimal numbers in binary format, but shouldn't the errors be equal regardless of the method used? See my example: > options(digits=22) > x=rep(.1,10) > x [1] 0.10000000000000001 0.10000000000000001

Hi, I don't know much about the RGL package, and I have read the documentation and tried some parameters, with no luck... I would like to generate a movie from a 3D object (code below), where the vortex A is closer to the observer, and then the object rotates and the B vortex gets closer. I would like to capture this movie to a file. By the way, I am not being able to insert unicode text

Hi, I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv

I've just found the lavaan package, and I really appreciate it, as it seems to succeed with models that were failing in sem::sem. I need some clarification, however, in the output, and I was hoping the list could help me. I'll go with the standard example from the help documentation, as my problem is much larger but no more complicated than that. My question is, why is there one latent

Hi, I would like to fit Logit models for ordered data, such as those suggested by Greene (2003), p. 736. Does anyone suggests any package in R for that? By the way, my dependent variable is ordinal and my independent variables are ratio/intervalar. Thanks, Iuri. Greene, W. H. Econometric Analysis. Upper Saddle River, NJ: Prentice Hall, 2003

I am a new user of the function sem in package sem and lavaan for structural equation modeling 1. I don?t know what is the difference between this function and CFA function, I know that cfa for confirmatory analysis but I don?t know what is the difference between confirmatory analysis and structural equation modeling in the package lavaan. 2. I have data that I want to analyse but I have some

What??s happening with this following code: require(sem) Celpe.Mod.RAM <- matrix(c( # path parametro Inicio "Produ????o -> T1", "gamma.11", NA, "Produ????o -> T2", "gamma.12", NA,

Hello, I'm trying to fit a pretty simple confirmatory factor analysis using the sem package. There's a CFA example in the examples, which is helpful, but the output for my (failing) model is hard to understand. I'd be interested in any other ways to do a CFA in R, if this proves troublesome. The CFA is replicating a 5 uncorrelated-factor structure (for those interested, it is a

Hello, I use the function sem the following way sem.mod <- sem(model, mod.cov, N=109) where the variables are modelled: Z -> M Z -> I Z -> R M <-> M I <-> I R <-> R Z <-> Z The output is ... Normalized Residuals Min. 1st Qu. Median Mean 3rd Qu. Max. -7.3300 -0.2750 -0.2670 -0.1290 -0.0369 9.0300 Parameter Estimates Estimate Std Error z value Pr(>|z|)

Hi, I'm doing a k-means cluster with 6 clusters and 15 variables. Any suggestions on how to plot the results? I've tried the standard xy plot, but couldn't get much of it. Thansk in advance, Iuri.

Hello. I started using the sem package in R and after a lot of searching and trying things I am still having difficulty. I get the following error message when I use the sem() function: Warning message: 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. I started with a

Hi there, Quick question about the output from the sem() function in the library of the same name. If I am getting probabilities >0.05 for some of my estimates of path coefficients, I'm assuming the interpretation here is that the coefficient is not significantly different from zero, correct? In that case, might it make sense that I should disregard path coefficients between

Hi, I am trying to compare two 3D plots. For that, I am trying to use the "pch" parameter in the "points3d" function, but it is not working. Is it implemented? Any suggestion? Here goes a reproducible code. I wanted the second plot having different symbols for the points. x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2), matrix(rnorm(100, mean = 1, sd = 0.3),

Sorry for the cross-posting, but I would like to know if anyone is aware of a package in R for this. ---------- Forwarded message ---------- From: John Antonakis Sent: Sunday, April 10, 2011 3:26 PM To: RMNET Subject: Meta-analysis of a correlation matrix (correct thread title) Hi: Does anyone know of good program that can do a meta-analytic multiple regression (with multiple correlated