mxComputeConfidenceInterval: Find likelihood-based confidence intervals

In OpenMx: Extended Structural Equation Modelling

Find likelihood-based 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 non-linear constraints. However, the available optimizers (CSOLNP, SLSQP, and NPSOL) often have difficulty with non-linear 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 run-time diagnostic output. Set to zero to disable
engine	\lifecycle deprecated
fitfunction	the name of the deviance function
tolerance	\lifecycle deprecated
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), 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.

OpenMx documentation built on Oct. 19, 2024, 9:06 a.m.