EM Control | R Documentation |
The .EMControl
generates an EM control (.EMC
)
controlling the options and conditions of EM algorithms,
i.e. this function generate a default template.
One can either modify .EMC
or employ this function to
control EM algorithms.
By default, .EMC
, .EMC.Rnd
, and .EC.Rndp
are
three native controllers as the EMCluster is loaded.
.EMControl(alpha = 0.99, short.iter = 200, short.eps = 1e-2,
fixed.iter = 1, n.candidate = 3,
em.iter = 1000, em.eps = 1e-6, exhaust.iter = 5)
.EMC
.EMC.Rnd
.EMC.Rndp
alpha |
only used in |
short.iter |
number of short-EM steps, default = 200. |
short.eps |
tolerance of short-EM steps, default = 1e-2. |
fixed.iter |
fixed iterations of EM for "RndEM" initialization, default = 1. |
n.candidate |
reserved for other initialization methods (unimplemented). |
em.iter |
maximum number of long-EM steps, default = 1000. |
em.eps |
tolerance of long-EM steps, default = 1e-6. |
exhaust.iter |
number of iterations for "exhaustEM" initialization, default = 5. |
exhaust.iter
and fixed.iter
are used to control the
iterations of initialization procedures.
short.iter
and short.eps
are used to control the
short-EM iterations.
em.iter
and em.eps
are used to control the long-EM iterations.
Moeover, short.eps
and em.eps
are for checking convergence of
the iterations.
This function returns a list as .EMC
by default.
The .EMC.Rnd
is equal to .EMControl(short.eps = Inf)
and
usually used by the rand.EM
method.
The .EMC.Rndp
is equal to .EMControl(fixed.iter = 5)
where
each random initials run 5 EM iterations in the rand.EM
method.
Wei-Chen Chen wccsnow@gmail.com and Ranjan Maitra.
https://www.stat.iastate.edu/people/ranjan-maitra
init.EM
, emcluster
.
library(EMCluster, quietly = TRUE)
.EMC <- .EMControl()
.EMC.Rnd <- .EMControl(short.eps = Inf)
.EMC.Rndp <- .EMControl(fixed.iter = 5)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.