FactorHet_init {FactorHet} | R Documentation |
Arguments for initializing FactorHet
Description
A set of arguments that govern the initialization of FactorHet
.
Use FactorHet_control
to set arguments around estimation.
FactorHet_mbo
ignores many of these arguments as it uses a
single fixed initialization set by FactorHet_mbo_control
. All
arguments have default values.
Usage
FactorHet_init(
short_EM = FALSE,
short_EM_it = 40,
short_EM_init = "random_member",
short_EM_pi = NULL,
force_rep = FALSE,
verbose = FALSE,
short_EM_beta_method = "cpp",
short_EM_cg_it = 10,
nrep = 5,
debug_repeat = FALSE,
plot_repeat = FALSE,
return_all = FALSE
)
Arguments
short_EM |
A logical value indicating whether "short EM" should be used.
The default value is |
short_EM_it |
A numerical value of the number of iterations to use for each "short" run of the EM algorithm. The default is 40. |
short_EM_init |
An argument that sets the initialization procedure for
"short EM". It must be some non-deterministic procedure that is valid in
|
short_EM_pi |
An argument for the maximum number of iterations for the
moderator updates to use for each "short" run of the EM algorithm. The
default is |
force_rep |
A logical value for whether to repeat the algorithm if
|
verbose |
A logical value to print more information about the progress
of each iteration. The default is |
short_EM_beta_method |
An argument for the update method for |
short_EM_cg_it |
An argument for the number of conjugate gradient
iterations to use if |
nrep |
An integer value of the number of random iterations or runs of "short EM" should be used. The default value is 5. |
debug_repeat |
A logical value for whether to debug the repeated runs.
The default is |
plot_repeat |
A logical value for whether to plot the trajectory of the
log-posterior for each run. The default is |
return_all |
A logical value for whether to return all repetitions of
the model versus the one with the highest log-posterior. The default is
|
Value
FactorHet_init
returns a named list containing the elements
listed in "Arguments".
References
Biernacki, Christophe, Gilles Celeux, and Gérard Govaert. "Choosing Starting Values for the EM algorithm for Getting the Highest Likelihood in Multivariate Gaussian Mixture Models." 2003. Computational Statistics & Data Analysis. 41(3-4):561-575.
Examples
str(FactorHet_init())