QLMDe_stepK | R Documentation |
K
To compare gh
-parsimonious models of Tukey g
-&-h
mixtures with different number of components K
(up to a user-specified K_\text{max}
)
and select the optimal number of components.
QLMDe_stepK(
x,
distname = c("GH", "norm"),
data.name = deparse1(substitute(x)),
Kmax = 3L,
test = c("BIC", "AIC"),
direction = c("forward", "backward"),
...
)
x |
numeric vector, observations |
distname , data.name |
character scalars, see parameters of the same names in function QLMDe |
Kmax |
integer scalar |
test |
character scalar, criterion to be used, either Akaike's information criterion AIC, or Bayesian information criterion BIC (default). |
direction |
character scalar, direct of selection in function step_fmx,
either |
... |
additional parameters |
Function QLMDe_stepK compares the gh
-parsimonious models with different number of components K
,
and selects the optimal number of components using BIC (default) or AIC.
The forward selection starts with finding the gh
-parsimonious model (via function step_fmx)
at K = 1
.
Let the current number of component be K^c
.
We compare the gh
-parsimonious models of K^c+1
and K^c
component, respectively,
using BIC or AIC.
If K^c
is preferred, then the forward selection is stopped, and K^c
is considered the
optimal number of components.
If K^c+1
is preferred, then
the forward selection is stopped if K^c+1=K_{max}
,
otherwise update K^c
with K_c+1
and repeat the previous steps.
Function QLMDe_stepK returns an object of S3 class 'stepK'
,
which is a list of selected models (in reversed order) with attribute(s)
'direction'
and
'test'
.
data(bmi, package = 'mixsmsn')
hist(x <- bmi[[1L]])
QLMDe_stepK(x, distname = 'GH', Kmax = 2L)
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