R: Find likelihood-based confidence intervals

mxComputeConfidenceInterval {OpenMx} | R Documentation

## 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` | deprecated
`fitfunction` | the name of the deviance function
`tolerance` | 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.

[Package OpenMx version 2.19.5 Index]