fit_mvnorm: Fit multivariate normal distribution to data
In ashiklom/mvtraits: Fit multivariate models to partially missing trait data

Fit multivariate normal distribution to data

fit_mvnorm(dat, niter = 5000, priors = list(), inits = list(),
  nchains = 3, autofit = FALSE, max_attempts = 10,
  keep_samples = Inf, threshold = 1.15, save_progress = NULL,
  progress = NULL)

`dat`	Input data, as a `data.frame` or matrix
`niter`	Number of iterations per attempt fitting attempt.
`priors`	Named list of prior overrides. See `gibbs_default_priors()` for details.
`inits`	Named list of initial conditions
`nchains`	Number of chains (default = 3)
`autofit`	(Logical) If `TRUE`, keep re-trying until model has converged (default = `FALSE`)
`max_attempts`	Maximum number of attempts for `autofit` (default = 10)
`keep_samples`	Maximum number of samples to keep in results (default = Inf)
`threshold`	Gelman PSRF threshold for convergence (default = 1.15)
`save_progress`	If not `NULL`, path to file in which to save current
`progress`	(Logical) If `TRUE`, display a progress bar while sampling chain status between fitting attempts. If `NULL` (default), default to `TRUE` if using a sequential `future::plan` and `FALSE`
`parallel`	(Logical) Whether or not to run chains in parallel (default = `TRUE`)

List containing a tidy summary table of the fitted parameters (summary_table), a list of the summary statistics (stats), and the raw MCMC samples as an mcmc.list (samples).

ashiklom/mvtraits documentation built on Sept. 4, 2019, 7:43 a.m.