mixtools: regmixEM – R documentation

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mixtools

regmixEM

EM Algorithm for Mixtures of Regressions

Description

Returns EM algorithm output for mixtures of multiple regressions with arbitrarily many components.

Usage

regmixEM(y, x, lambda = NULL, beta = NULL, sigma = NULL, k = 2,
         addintercept = TRUE, arbmean = TRUE, arbvar = TRUE, 
         epsilon = 1e-08, maxit = 10000, verb = FALSE)

Arguments

`y`	An n-vector of response values.
`x`	An nxp matrix of predictors. See `addintercept` below.
`lambda`	Initial value of mixing proportions. Entries should sum to 1. This determines number of components. If NULL, then `lambda` is random from uniform Dirichlet and number of components is determined by `beta`.
`beta`	Initial value of `beta` parameters. Should be a pxk matrix, where p is the number of columns of x and k is number of components. If NULL, then `beta` has standard normal entries according to a binning method done on the data. If both `lambda` and `beta` are NULL, then number of components is determined by `sigma`.
`sigma`	A vector of standard deviations. If NULL, then 1/`sigma`^2 has random standard exponential entries according to a binning method done on the data. If `lambda`, `beta`, and `sigma` are NULL, then number of components is determined by `k`.
`k`	Number of components. Ignored unless all of `lambda`, `beta`, and `sigma` are NULL.
`addintercept`	If TRUE, a column of ones is appended to the x matrix before the value of p is calculated.
`arbmean`	If TRUE, each mixture component is assumed to have a different set of regression coefficients (i.e., the `beta`s).
`arbvar`	If TRUE, each mixture component is assumed to have a different `sigma`.
`epsilon`	The convergence criterion.
`maxit`	The maximum number of iterations.
`verb`	If TRUE, then various updates are printed during each iteration of the algorithm.

Value

regmixEM returns a list of class mixEM with items:

`x`	The set of predictors (which includes a column of 1's if `addintercept` = TRUE).
`y`	The response values.
`lambda`	The final mixing proportions.
`beta`	The final regression coefficients.
`sigma`	The final standard deviations. If `arbmean` = FALSE, then only the smallest standard deviation is returned. See `scale` below.
`scale`	If `arbmean` = FALSE, then the scale factor for the component standard deviations is returned. Otherwise, this is omitted from the output.
`loglik`	The final log-likelihood.
`posterior`	An nxk matrix of posterior probabilities for observations.
`all.loglik`	A vector of each iteration's log-likelihood.
`restarts`	The number of times the algorithm restarted due to unacceptable choice of initial values.
`ft`	A character vector giving the name of the function.

References

de Veaux, R. D. (1989), Mixtures of Linear Regressions, Computational Statistics and Data Analysis 8, 227-245.

Hurn, M., Justel, A. and Robert, C. P. (2003) Estimating Mixtures of Regressions, Journal of Computational and Graphical Statistics 12(1), 55–79.

McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models, John Wiley \& Sons, Inc.

Examples

## EM output for NOdata.
 
data(NOdata)
attach(NOdata)
set.seed(100)
em.out <- regmixEM(Equivalence, NO, verb = TRUE, epsilon = 1e-04)
em.out[3:6]

mixtools

Tools for Analyzing Finite Mixture Models

v1.2.0

GPL (>= 2)

Authors

Derek Young [aut, cre] (<https://orcid.org/0000-0002-3048-3803>), Tatiana Benaglia [aut], Didier Chauveau [aut], David Hunter [aut], Ryan Elmore [ctb], Thomas Hettmansperger [ctb], Hoben Thomas [ctb], Fengjuan Xuan [ctb]

Initial release

2020-02-05

regmixEM

Description

Usage

Arguments

Value

References

See Also

Examples

mixtools

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