On Tue, Aug 09, 2011 at 01:49:13PM +0200, yrosseel wrote: > > >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 regression line to your model syntax with > 'fire.setting' as the dependent variable: > > fire.setting ~ x1 + x2 + x3 > > were x1-x3 are additional predictors that might influence the > variable 'fire.setting'.
Can I include criminality among those and thereby get the common part
of criminality and fire.setting "out of the way"?
I tried adding the following regression formula:
fire.setting ~ parental.migration + parental.class + sex.girl +
priviledged.parents + school.adaption + school.grades +
criminality
but I got:
Error in solve.default(E) :
Lapack routine dgesv: system is exactly singular
[lavaan message:] could not compute standard errors!
You can still request a summary of the fit to inspect
the current estimates of the parameters.
However, the fit-object has regression estimates were criminality
seems to have about the same size as I would have thought, given the
covariation of fire.setting and criminality.
Estimate Std.err Z-value P(>|z|) Std.lv Std.all
fire.setting ~
parental.migr 0.001 0.001 0.003
parental.clas -0.000 -0.000 -0.000
sex.girl -0.015 -0.015 -0.019
priviledged.p 0.066 0.015 0.039
school.adapti 0.004 0.002 0.005
school.grades -0.012 -0.010 -0.026
criminality 0.327 0.191 0.505
Are the other estimates reasonable estimates of the part of variation
in fire-setting that does not co-variate with criminality?
--
Hans Ekbrand
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