[R] easy way to fit saturated model in sem package?
luke-tierney at uiowa.edu
luke-tierney at uiowa.edu
Fri Jul 13 18:05:28 CEST 2012
They look fine to me.
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
> 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,
> On Thu, Jul 12, 2012 at 8:20 AM, John Fox <jfox at 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
>> 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
>> John Fox
>> Senator William McMaster
>> Professor of Social Statistics
>> Department of Sociology
>> McMaster University
>> Hamilton, Ontario, Canada
>>> -----Original Message-----
>>> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
>>> project.org] On Behalf Of Joshua Wiley
>>> Sent: July-12-12 2:53 AM
>>> To: r-help at r-project.org
>>> Cc: John Fox
>>> Subject: [R] easy way to fit saturated model in sem package?
>>> 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 <-
>>> 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).
>>> Joshua Wiley
>>> Ph.D. Student, Health Psychology
>>> Programmer Analyst II, Statistical Consulting Group University of
>>> California, Los Angeles https://joshuawiley.com/
>>> R-help at r-project.org mailing list
>>> PLEASE do read the posting guide http://www.R-project.org/posting-
>>> and provide commented, minimal, self-contained, reproducible code.
Chair, Statistics and Actuarial Science
Ralph E. Wareham Professor of Mathematical Sciences
University of Iowa Phone: 319-335-3386
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