constrained_lmm_mcem: Fit monotone-constrained polynomial LMM with an MCEM...
In bonStats/gcreg: General Constraint REGression

Description Usage Arguments Details Value

View source: R/mixed-effect-mcem-constrained-polynomial-model.R

EXPIREMENTAL: Fit polynomial constrained mixed effect model with BOTH monotonic mean and monotonic random effects. For use with block-random effects (non-crossed), e.g. subject-specific random effects. Uses a Monte Carlo expectation step.

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constrained_lmm_mcem(model, maxit = 200, tol, start = NULL, verbose = F,
  save_steps = F, check_up = F, parallel = F, cores = 1,
  ni_ssize = 20000)

`model`	model specification made by `make_em_model_specs`.
`maxit`	maximum number of iterations.
`tol`	relative tolerance for convergence of parameter values.
`start`	start values for parameters. Defaults to NULL.
`verbose`	output current iteration and quasi-log-likelihood value.
`save_steps`	should each em step be saved and return in a list?
`check_up`	should the algorithm check if the quasi-log-likelihood is improving each iteration?
`parallel`	Execute Monte Carlo expectations in parallel. This will turn off verbose settings for expectations. ONLY IMPLEMENTED FOR LINUX/MAC
`cores`	Number of cores to use.
`ni_ssize`	Number of realisations to used for numerical integration.

The interface and default actions of this function are under development and may change without warning. See the mixed effects vignette for usage details.

list including estimated mixed effects model.

bonStats/gcreg documentation built on May 20, 2019, 5:44 p.m.