On 08/12/2013 05:55 PM, Pablo Menese Camargo wrote:
fitcfa - cfa(mcfa, data = data_analisis)
summary(fitcfa, standardized = TRUE, fit.measure=TRUE)
standardizedSolution(fitcfa, type = std.all)
anyone know what y should do to apply polychoric?
See the manual:
On 02/21/2013 03:59 PM, Marios wrote:
My question...I would like to calculate the total indirect effects of
all variables on the right-hand-side of the regression eqn's so that i can
work out the total effect (indirect effects + direct effect)
I know the direct effect and i can calculate
On 02/22/2013 11:40 AM, Marios wrote:
Thank you very much Yves!
I have managed to get the total indirect effects that i wanted but it
seems to only work on the unstandardized coefficients. I use
standardized =TRUE in the summary command but the std.all column
has the same values as the Estimate
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.
That is a lot of variables, and a rather small sample size (for
The reason I was working with a correlation matrix is because I wanted
to calculate polychoric correlations first before submitting it to the
actual sem command. I was unsure whether R would use polychoric
correlations when indicating which of the variables are ordered. A final
question on the
On 10/31/2012 02:47 PM, sylvain.giroud wrote:
Dear R-users,
Does somebody know what does the Estimate reported by the Lavaan model
tell us?
I assume this tells the relative strength of the dyadic relations.
The 'Estimate' column contains the estimated model parameters. There are
many
On 10/15/2012 08:28 AM, PIKAL Petr wrote:
Hi
-Original Message- From: r-help-boun...@r-project.org
[mailto:r-help-bounces@r- project.org] On Behalf Of Gunsalus,
Catherine Sent: Friday, October 12, 2012 8:52 PM To:
r-help@r-project.org Subject: [R] Error in rowMeans function
Hello, I
On 10/07/2012 02:17 AM, Elaine Kuo wrote:
Hello,
This is Elaine.
I am trying a path analysis using lavaan Package.
There are three explanatory variables: X, Z, and M.
The response variable is Y.
A, b, and c have direct effects on Y.
On the other hand, X and Z also have direct effects on M.
On 07/30/2012 11:00 PM, Luna wrote:
Dear R users,
I have a hard time interpreting the covariances in the parameter estimates
output (standardized), even in the example documented (PoliticalDemocracy).
Can anyone tell me if the estimated covariances are residual covariances
(unexplained by the
I will check out the lavaan package.
Dear Joshua,
The lavaan package may help you. The FIML estimator typically starts
with the EM algorithm to estimate the moments of the unrestricted model.
There is no 'one-shot' function for it, at the moment, but if you only
need those moments, you
On 07/20/2012 10:35 PM, Andrew Miles wrote:
Hello!
I am trying to reproduce (for a publication) analyses that I ran
several months ago using lavaan, I'm not sure which version, probably
0.4-12. A sample model is given below:
pathmod='mh30days.log.w2 ~ mh30days.log + joingroup + leavegroup +
On 12/01/2011 05:25 PM, Dustin Fife wrote:
What is your goal? I have used and like mice pretty well, but using
mice + sem to try to address missingness seems like more work than
using FIML in OpenMx or lavaan to try to address it. Is there a
reason you want to use the sem package or a reason
Maybe this kind of usage of lavaan is not very common, but in order to
help others in my situation, is this documented somewhere? My
understanding of latent variable analysis is indeed limited, but I did
not understand that lavaan worked liked this when I read the
documentation.
This is not
My question is: how can I analyse the part of the variation in
fire.setting that is not included in the latent variable criminality?
Ideally I would want a new variable that captures just this. Then I
could model regressions with this variable as the dependent variable.
You can add a
Can I include criminality among those and thereby get the common part
of criminality and fire.setting out of the way?
No. You already regress fire.setting on criminality since it is an
indicator in the measurement model of criminality. In other words, the
'criminality' part is already
On 06/08/2011 11:56 PM, R Help wrote:
Yes, that is the difference. For the last SEM I built I fixed the
factor variances to 1, and I think that's what I want to do for the
CFA I'm doing now. Does that make sense for a CFA?
If you have a latent variable in your model (like a factor in CFA),
On 06/09/2011 05:21 PM, R Help wrote:
Ok, I think this is the last question I have. My model is producing
an estimate of intercepts for my variables along with my loadings.
From the documentation it appears that this is controlled by the
meanstructure option in cfa. It says that setting it
On 06/09/2011 06:06 PM, R Help wrote:
I am using missing = 'fiml', which would require estimating intercepts.
I figured they would effect my overall model fit, but can I still
estimate my loading coefficients the same way?
Yes, no problem.
Yves Rosseel
http://lavaan.org
Dear Alain,
As for the first error (sample covariance can not be inverted): Mike
is right: with only 10 observations and 16 variables, the ML estimation
of the sample cov produces a covariance matrix that is not positive
definite, and hence the inversion (deliberately) fails.
The lesson for
On 04/04/2011 07:14 PM, jouba wrote:
Thanks you for your response
For lavaan package can i have more information about this example you have
applied in the section 7
the meanings of The variables (c1,c2,c3,c4, i ,s ,x1,x2)
I think i have need more information to learn more about how able
On 04/03/2011 09:38 PM, jouba wrote:
Daer all, I have a question concerning longitudinal data: When we
have a longitudinal data and we have to do sem analysis there is in
the package lavaan some functions,options in this package that help
to do this or we can treat these data like non
On 03/29/2011 10:49 PM, jouba wrote:
Dear all
I have an error mesage
« error message : the MLM estimator can not be used when data are incomplete »
When i use the function sem(package lavaan) and when i fill the paramters
estimator and missing
estimator=MLM, missing=ml
i understand
On 03/28/2011 04:18 AM, jouba wrote:
Jeremy thanks a lot for your response I have read sem package help
and I currently reading the help of lavaan I see that there is also
an other function called lavaan can do the SEM analysis So I wonder
what is the difference between this function and the
the cfa() function is in the 'lavaan' package
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Rob,
If 'sex' is indeed an exogenous variable (ie. predictor only), you can
simply code it as (1=male, 2=female) and use it as a covariate in any sem
model. In lavaan, you can explicitly use the argument 'fixed.x=TRUE', which
will regard all exogenous covariates as fixed variables. Their
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