They look fine to me.
luke
On Fri, 13 Jul 2012, Joshua Wiley wrote:
Dear John,
Thanks very much for the reply. Looking at the optimizers, I had
thought that the objectiveML did what I wanted. I appreciate the
clarification.
I think that multiple imputation is more flexible in some ways because
you can easy create different models for every variable. At the same
time, if the assumptions hold, FIML is equivalent to multiple
imputation, and considerably more convenient. Further, I suspect that
in many circumstances, either option is equal to or better than
listwise deletion.
In my case, I am working on some tools primarily for data exploration,
in a SEM context (some characteristics of individual variables and
then covariance/correlation matrices, clustering, etc.) and hoped to
include listwise/pairwise/FIML as options.
I will check out the lavaan package.
Thanks again for your time,
Josh
On Thu, Jul 12, 2012 at 8:20 AM, John Fox <j...@mcmaster.ca> wrote:
Dear Joshua,
If I understand correctly what you want to do, the sem package won't do it.
That is, the sem() function won't do what often is called FIML estimation
for models with missing data. I've been thinking about implementing this
feature, and don't think that it would be too difficult, but I can't promise
when and if I'll get to it. You might also take a look at the lavaan
package.
As well, I must admit to some skepticism about the FIML estimator, as
opposed to approaches such as multiple imputation of missing data. I suspect
that the former is more sensitive than the latter to the assumption of
multinormality.
Best,
John
--------------------------------
John Fox
Senator William McMaster
Professor of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
project.org] On Behalf Of Joshua Wiley
Sent: July-12-12 2:53 AM
To: r-help@r-project.org
Cc: John Fox
Subject: [R] easy way to fit saturated model in sem package?
Hi,
I am wondering if anyone knows of an easy way to fit a saturated model
using the sem package on raw data? Say the data were:
mtcars[, c("mpg", "hp", "wt")]
The model would estimate the three means (intercepts) of c("mpg", "hp",
"wt"). The variances of c("mpg", "hp", "wt"). The covariance of mpg
with hp and wt and the covariance of hp with wt.
I am interested in this because I want to obtain the MLE mean vector
and covariance matrix when there is missing data (i.e., the sum of the
case wise likelihoods or so-called full information maximum
likelihood). Here is exemplary missing data:
dat <- as.matrix(mtcars[, c("mpg", "hp", "wt")])
dat[sample(length(dat), length(dat) * .25)] <- NA dat <-
as.data.frame(dat)
It is not too difficult to write a wrapper that does this in the OpenMx
package because you can easily define paths using vectors and get all
pairwise combinations using:
combn(c("mpg", "hp", "wt"), 2)
but I would prefer to use the sem package, because OpenMx does not work
on 64 bit versions of R for Windows x64 and is not available from CRAN
presently. Obviously it is not difficult to write out the model, but I
am hoping to bundle this in a function that for some arbitrary data,
will return the FIML estimated covariance (and correlation matrix).
Alternately, if there are any functions/packages that just return FIML
estimates of a covariance matrix from raw data, that would be great
(but googling and using findFn() from the sos package did not turn up
good results).
Thanks!
Josh
--
Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, Statistical Consulting Group University of
California, Los Angeles https://joshuawiley.com/
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