search for: specifyequations

Dear R users, Version 2.1-1 of the sem package, for structural equation modeling, is now on CRAN. Unlike version 2.0-0, which was a major overhaul of the package, version 2.1-1 just sprinkles some syntactic sugar on it, introducing the specifyEquations() and cfa() functions; specifyEquations() supports model specification in equation (rather than path) format, and cfa() facilitates compact specification of simple confirmatory factor analysis models. For example, from ?sem, the Duncan, Haller, and Portes peer-influences model can now be specifie...

Dear R users, Version 2.1-1 of the sem package, for structural equation modeling, is now on CRAN. Unlike version 2.0-0, which was a major overhaul of the package, version 2.1-1 just sprinkles some syntactic sugar on it, introducing the specifyEquations() and cfa() functions; specifyEquations() supports model specification in equation (rather than path) format, and cfa() facilitates compact specification of simple confirmatory factor analysis models. For example, from ?sem, the Duncan, Haller, and Portes peer-influences model can now be specifie...

...e. Should I just create two files with the model equations? But then how do I use these 2 files as an input to the multigroupModel or sem functions? I tried the following but got the error message {Error in model[, 2] : incorrect number of dimensions}. Here is a sample of the code: mod.QT.1 <- specifyEquations(...) # model equations for category 1 mod.QT.2 <- specifyEquations(...) # model equations for category 2 Lst.model <- list(mod.QT.1, mod.QT.2) mod.mg <- multigroupModel(Lst.model, groups=c("Driver", "Nondriver")) sem.QT <- sem(mod.mg, data=QTrav, group="...

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,

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 have conducted an SEM in which the resultant standardized path coefficients are much higher than would be expected from the raw correlation matrix. To explore further, I stripped the model down to a simple bivariate relationship between two variables (NDVI, and species richness), where it's my understanding that the SEM's standardized path coefficient should equal the correlation