step_fmx {QuantileGH} | R Documentation |
Forward Selection of gh
-parsimonious Model with Fixed Number of Components K
Description
To select the gh
-parsimonious mixture model,
i.e., with some g
and/or h
parameters equal to zero,
conditionally on a fixed number of components K
.
Usage
step_fmx(
object,
test = c("BIC", "AIC"),
direction = c("forward", "backward"),
...
)
Arguments
object |
fmx object |
test |
character scalar, criterion to be used, either Akaike's information criterion AIC-like, or Bayesian information criterion BIC-like (default). |
direction |
character scalar, |
... |
additional parameters, currently not in use |
Details
The algorithm starts with quantile least Mahalanobis distance estimates
of either the full mixture of Tukey g
-&-h
distributions model, or
a constrained model (i.e., some g
and/or h
parameters equal to zero according to the user input).
Next, each of the non-zero g
and/or h
parameters is tested using the likelihood ratio test.
If all tested g
and/or h
parameters are significantly different from zero at the level 0.05
the algorithm is stopped and the initial model is considered gh
-parsimonious.
Otherwise, the g
or h
parameter with the largest p-value is constrained to zero
for the next iteration of the algorithm.
The algorithm iterates until only significantly-different-from-zero g
and h
parameters
are retained, which corresponds to gh
-parsimonious Tukey g
-&-h
mixture model.
Value
Function step_fmx returns an object of S3 class 'step_fmx'
,
which is a list of selected models (in reversed order) with attribute(s)
'direction'
and
'test'
.