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 non-linear constraints. However, the available optimizers (CSOLNP, SLSQP, and NPSOL) often have difficulty with non-linear constraints.
mxComputeConfidenceInterval(
plan,
...,
freeSet = NA_character_,
verbose = 0L,
engine = NULL,
fitfunction = "fitfunction",
tolerance = NA_real_,
constraintType = "none"
)
compute plan to optimize the model
Not used. Forces remaining arguments to be specified by name.
names of matrices containing free variables
integer. Level of run-time diagnostic output. Set to zero to disable
deprecated
the name of the deviance function
deprecated
one of c('ineq', 'none')
Neale, M. C. & Miller M. B. (1997). The use of likelihood based confidence intervals in genetic models. Behavior Genetics, 27(2), 113-120.
Pek, J. & Wu, H. (2015). Profile likelihood-based confidence intervals and regions for structural equation models. Psychometrika, 80(4), 1123-1145.
Wu, H. & Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior genetics, 42(6), 886-898.