summary.gaussmodel

Summary of <code>gaussmodel</code> object, including a mean and
a standard deviation and quantiles.
If some estimation has done, also print out estimates, up to <code>nmax</code>
number of them.

The models of probability density functions are Gaussian or
exponential distributions with polynomial correction terms.
Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian
or exponential distributions together with degrees of polynomials by
a grid search, and coefficient of polynomials by a variant of semidefinite
programming. It adopts Akaike Information Criterion for model selection.
See a vignette for a tutorial and more on our 'Github' repository
<https://github.com/tsuchiya-lab/dsdp/>.

Satoshi Kakihara

dsdp

Density Estimation with Semidefinite Programming

Takashi Tsuchiya

summary.gaussmodel function

<dl><dt>object</dt>
<dd><code>gaussmodel</code> object.</dd>
<dt>nmax</dt>
<dd>A number of estimates to show in the summary.
The default is 10.</dd>
<dt>estonly</dt>
<dd>Show only the results of estimates. The default value
is <code>FALSE</code>.</dd>
<dt>...</dt>
<dd>Arguments to be passed to or from other methods.</dd></dl>

Arguments

Summary of Gaussian-based model gaussmodel object — summary.gaussmodel

<dl>

<dt>object</dt>
<dd><code>gaussmodel</code> object.</dd>


<dt>nmax</dt>
<dd>A number of estimates to show in the summary.
The default is 10.</dd>


<dt>estonly</dt>
<dd>Show only the results of estimates. The default value
is <code>FALSE</code>.</dd>


<dt>...</dt>
<dd>Arguments to be passed to or from other methods.</dd>

</dl>

summary.gaussmodel: Summary of Gaussian-based model `gaussmodel` object

Description

Usage

Value

Arguments

See Also

Examples