mxComputeConfidenceInterval {OpenMx}  R Documentation 
Find likelihoodbased confidence intervals
Description
There are various equivalent ways to pose the optimization problems required to estimate confidence intervals. Most accurate solutions are achieved when the problem is posed using nonlinear constraints. However, the available optimizers (CSOLNP, SLSQP, and NPSOL) often have difficulty with nonlinear constraints.
Usage
mxComputeConfidenceInterval(
plan,
...,
freeSet = NA_character_,
verbose = 0L,
engine = NULL,
fitfunction = "fitfunction",
tolerance = NA_real_,
constraintType = "none"
)
Arguments
plan 
compute plan to optimize the model 
... 
Not used. Forces remaining arguments to be specified by name. 
freeSet 
names of matrices containing free variables 
verbose 
integer. Level of runtime diagnostic output. Set to zero to disable 
engine 

fitfunction 
the name of the deviance function 
tolerance 

constraintType 
one of c('ineq', 'none') 
References
Neale, M. C. & Miller M. B. (1997). The use of likelihood based confidence intervals in genetic models. Behavior Genetics, 27(2), 113120.
Pek, J. & Wu, H. (2015). Profile likelihoodbased confidence intervals and regions for structural equation models. Psychometrika, 80(4), 11231145.
Wu, H. & Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior genetics, 42(6), 886898.