lambda

Return local estimates of the mixing proportions from each component
 of a mixture of regressions model using output from an EM algorithm.

internal

Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).

Derek S. Young

mixtools

Tools for Analyzing Finite Mixture Models

Derek Young

Tatiana Benaglia

Didier Chauveau

David Hunter

Kedai Cheng

Ryan Elmore

Thomas Hettmansperger

Hoben Thomas

Fengjuan Xuan

lambda function

<dl> <dt>z</dt>
<dd>An nxk matrix of posterior probabilities obtained from the EM
 algorithm.</dd> <dt>x</dt>
<dd>A vector of values for which the local estimation is calculated.</dd> <dt>xi</dt>
<dd>An nx(p-1) matrix of the predictor values.</dd> <dt>h</dt>
<dd>The bandwidth controlling the size of the window used for the
 local estimation.</dd> <dt>kernel</dt>
<dd>The type of kernel to be used for the local estimation.</dd> <dt>g</dt>
<dd>A shape parameter required for the symmetric beta kernel. The default
 is <code>g</code> = 0 which yields the uniform kernel. Some common values are <code>g</code> = 1 for the
 Epanechnikov kernel, <code>g</code> = 2 for the biweight kernel, and <code>g</code> = 3 for the triweight kernel.</dd></dl>

Arguments

Local Estimation for Lambda in Mixtures of Regressions — lambda

<dl>

 <dt>z</dt>
<dd>An nxk matrix of posterior probabilities obtained from the EM
 algorithm.</dd>

 <dt>x</dt>
<dd>A vector of values for which the local estimation is calculated.</dd>

 <dt>xi</dt>
<dd>An nx(p-1) matrix of the predictor values.</dd>

 <dt>h</dt>
<dd>The bandwidth controlling the size of the window used for the
 local estimation.</dd>

 <dt>kernel</dt>
<dd>The type of kernel to be used for the local estimation.</dd>

 <dt>g</dt>
<dd>A shape parameter required for the symmetric beta kernel. The default
 is <code>g</code> = 0 which yields the uniform kernel. Some common values are <code>g</code> = 1 for the
 Epanechnikov kernel, <code>g</code> = 2 for the biweight kernel, and <code>g</code> = 3 for the triweight kernel.</dd>

</dl>

Local Estimation for Lambda in Mixtures of Regressions

lambda: Local Estimation for Lambda in Mixtures of Regressions

Description

Usage

Value

Arguments

See Also