*! version 1.1.13 04jun2007

help for confa postestimationauthor: Stas Kolenikov
also see: confa, bollenstine

Postestimation tools for confa

The following commands are available after confa:

command description

estat fitindices fit indices

estat aic AIC

estat bic BIC

estat correlate correlations of factors and measurement errors

predict factor scores

Special interest postestimation commands

These commands provide some additional post-estimation output.

estatcorrelate transforms the covariance parameters
into correlations for factor covariances and measurement error covariances.
The delta method standard errors are given; for correlations close to
plus or minus 1, the confidence intervals may extend beyond the range of
admissible values. Additional options are allowed:

level(#) specifies the CI level

bound provides an alternative CI based on Fisher's z-transform
(arctanh) of the correlation coefficient. It guarantees that the
end points of the interval are in (-1,1) range, which may not
produce desirable results for Heywood cases.

estat aic and estat bic compute the Akaike and Schwarz Bayesian
information criteria.

estatfitindices computes, prints, and saves into r() results
a number of traditional fit indices. The following options of estat fitindices
request specific indices:

option fit index

aic AIC, Akaike information criteria

bic BIC, Schwarz Bayesian information criteria

rmsea RMSEA, root mean squared error of approximation

rmsr RMSR, root mean square residual

tli TLI, Tucker-Lewis index

cfi CFI, comparative fit index

_all all of the above indices, the default

Syntax for predict

predict [type] newvarlist [if] [in] [, scoring_method]

predict can be used to create factor scores following confa.
The number of variables in newvarlist must be the same as the
number of factors in the model specification; all factors
are predicted at once by the relevant matrix formula, anyway.
The following methods are supported:

option factor scoring method

regression regression, or empirical Bayes, score

empiricalbayes alias for regression

ebayes alias for regression

mle MLE, or Bartlett score

bartlett MLE, or Bartlett score, alias for mle

Bollen-Stine bootstrap

bollenstine, reps(#)
saving(filename)
confaoptions(...)
bootstrap_options

bollenstine performs Bollen and Stine (1992) bootstrap.
The original data are rotated to conform to the fitted structure.
By default, bollenstine re-estimates the model
with rotated data, and uses the estimates as
starting values in each bootstrap iterations. It also rejects samples
where convergence was not achieved (implemented through
reject( e(converged) == 0) option supplied to
bootstrap).

The following options are supported:

reps(#) specifies the number of bootstrap replications.
The default is 200.

saving(filename) specifies the file
where the simulation results (the parameter estimates and the fit statistics)
are to be stored. The default is a temporary file that will
be deleted as soon as bollenstine finishes.

confaoptions(...) allows to transfer options
to confa. Some bootstrap replications produce non-convergent
samples that may never converge, so in order to speed up computations,
it might make sense to limit the number of iterations, say
with confaoptions( iter(20) ).

All non-standard model options, like unitvar or correlated, must be specified with
bollenstine to produce correct results!

All other options are assumed to be bootstrap_options
and passed through to bootstrap.

Example

. use http://web.missouri.edu/~kolenikovs/stata/hs-cfa.dta, clear

. confa (vis: x1 x2 x3) (text: x4 x5 x6) (math: x7 x8 x9), from(iv) corr(x7:x8)

. estat fit

. estat corr

. estat corr, bound

. predict fa1-fa3, reg

. predict fb1-fb3, bart

Also see

Online: confa, bollenstine.

References

Bollen, K. and Stine, R. (1992)
Bootstrapping Goodness of Fit Measures in Structural
Equation Models. Sociological Methods and Research, 21, 205--229.

Contact

Stas Kolenikov, kolenikovs at missouri.edu