try.flare: Mixtures of Regressions with Flare MM Algorithm

In mixtools: Tools for Analyzing Finite Mixture Models

Mixtures of Regressions with Flare MM Algorithm

Description

The function which flaremixEM actually calls. This only allows one barrier constant to be inputted at a time.

Usage

try.flare(y, x, lambda = NULL, beta = NULL, sigma = NULL, 
          alpha = NULL, nu = 1, epsilon = 1e-04, 
          maxit = 10000, verb = FALSE, restart = 50)

Arguments

y	An n-vector of response values.
x	An n-vector of predictor values. An intercept term will be added by default.
lambda	Initial value of mixing proportions. Entries should sum to 1.
beta	Initial value of beta parameters. Should be a 2x2 matrix where the columns corresond to the component.
sigma	A vector of standard deviations.
alpha	A scalar for the exponential component's rate.
nu	A scalar specifying the barrier constant to use.
epsilon	The convergence criterion.
maxit	The maximum number of iterations.
verb	If TRUE, then various updates are printed during each iteration of the algorithm.
restart	The number of times to restart the algorithm in case convergence is not attained. The default is 50.

Details

This usually is not called by the user. The user will likely want flaremixEM, which also has an example to demonstrate this algorithm.

Value

try.flare returns a list of class mixEM with items:

x	The set of predictors (which includes a column of 1's).
y	The response values.
posterior	An nx2 matrix of posterior probabilities for observations.
lambda	The final mixing proportions.
beta	The final regression coefficients.
sigma	The final standard deviations.
alpha	The final exponential rate.
loglik	The final log-likelihood.
all.loglik	A vector of each iteration's log-likelihood.
ft	A character vector giving the name of the function.

See Also

flaremixEM

mixtools documentation built on Dec. 5, 2022, 5:23 p.m.