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 +
alwaysgroup + grp.partic.w2 + black + age + bivoc + moved.conf +
local.noretired + retired + ds + ministrytime + hrswork +
nomoralescore.c + negint.c + cong.conflict.c + nomoraleXjoin +
nomoraleXleave + nomoraleXalways + negintXjoin + negintXleave +
negintXalways + conflictXjoin + conflictXleave + conflictXalways '
mod1 = sem(pathmod, data=sampledat, missing="fiml", se="robust")
At the time, the model ran fine. Now, using version 0.4-14, the
model returns all 0's for coefficients.
What happened is that since 0.4-14, lavaan tries to 'detect' models that
are just univariate regression, and internally calls lm.fit, instead of
the lavaan estimation engine, at least when the missing="ml" argument is
NOT used. (BTW, I fail to understand why you would use lavaan if you
just want to fit a univariate regression).
When missing="ml" is used, lavaan normally checks if you have fixed x
covariates (which you do), and if fixed.x=TRUE (which is the default).
In 0.4, lavaan internally switches to fixed.x=FALSE (which implicitly
assumes that all your predictors are continuous, but I assume you would
not using missing="ml" otherwise). Unfortunately, for the 'special' case
of univariate regression, it fails to do this. This behavior will likely
change in 0.5, where, by default, only endogenous/dependent variables
will be handled by missing="ml", not exogenous 'x' covariates.
To fix it: simply add the fixed.x=FALSE argument, or revert to 0.4-12 to
get the old behavior.
Hope this helps,
Yves.
http://lavaan.org
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