[R] easy way to fit saturated model in sem package?
ruipbarradas at sapo.pt
Thu Jul 12 17:34:30 CEST 2012
There's a package, lavaan, that implements FIML as an option of function
sem(). I have never used it, though, so I can't say much about it.
Hope this helps,
Em 12-07-2012 16:20, John Fox escreveu:
> 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.
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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