Dear Felipe,
Without the data or the input covariance or correlation matrix it's not
possible to say much. sem() would have complained if the input moment matrix
weren't positive-definite, so your check of the eigenvalues of the matrix isn't
providing additional information.
If you haven't al
You have a fairly large and complex model there. This sort of model
(almost) always causes problems.
I would try fitting one factor at a time. That might help you to
narrow down the problem. If one factor doesn't converge, the whole
model won't converge.
You might also consider joining the str
Someone can help me? I tried several things and always don't converge
I am making a confirmatory factor analysis model.
# Model
library(sem)
dados40.cov <- cov(dados40,method="spearman")
model.dados40 <- specify.model()
F1 -> Item11, lam11, NA
F1 -> Item31, lam31, NA
F1 -> Item36, lam36, NA
F1
Someone can help me? I tried several things and always don't converge
I am making a confirmatory factor analysis.
# Model
library(sem)
dados40.cov <- cov(dados40,method="spearman")
model.dados40 <- specify.model()
F1 -> Item11, lam11, NA
F1 -> Item31, lam31, NA
F1 -> Item36, lam36, NA
F1 -> I
Someone can help me? I tried several things and always don't converge
# Model
library(sem)
dados40.cov <- cov(dados40,method="spearman")
model.dados40 <- specify.model()
F1 -> Item11, lam11, NA
F1 -> Item31, lam31, NA
F1 -> Item36, lam36, NA
F1 -> Item54, lam54, NA
F1 -> Item63, lam63, NA
F1
Someone can help me? I tried several things and always don't converge
I am making a confirmatory factor analysis model
# Model
library(sem)
dados40.cov <- cov(dados40,method="spearman")
model.dados40 <- specify.model()
F1 -> Item11, lam11, NA
F1 -> Item31, lam31, N
6 matches
Mail list logo