constrained_lmm_em: Fit monotone-constrained polynomial LMM with an EM algorithm
In bonStats/gcreg: General Constraint REGression

Description Usage Arguments Details Value

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

EXPIREMENTAL: Fit polynomial constrained mixed effect model with monotonic mean and (potentially) monotonic random effects (r <= 2 for monotone random effects). See constrained_lmm_mcem for (r > 2). For use with block-random effects (non-crossed), e.g. subject-specific random effects.

1 2	constrained_lmm_em(model, maxit = 200, tol, start = NULL, verbose = F, save_steps = F, check_up = F)

`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?

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.