mxComputeConfidenceInterval {OpenMx} | R Documentation |
There are various equivalent ways to pose the optimization problems required to estimate confindence intervals. Most accurate solutions are achieved when the problem is posed using non-linear constraints. However, the available optimizers (NPSOL, SLSQP, and CSOLNP) often have difficulty with non-linear constraints.
mxComputeConfidenceInterval(plan, ..., freeSet = NA_character_, verbose = 0L, engine = NULL, fitfunction = "fitfunction", tolerance = NA_real_, constraintType = "none")
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 |
level of debugging output |
engine |
deprecated |
fitfunction |
the name of the deviance function |
tolerance |
deprecated |
constraintType |
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. Psychometrica, 80(4), 1123-1145.
Wu, H. & Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior genetics, 42(6), 886-898.