mixtools: regmixEM.lambda – R documentation

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regmixEM.lambda

EM Algorithm for Mixtures of Regressions with Local Lambda Estimates

Description

Returns output for one step of an EM algorithm output for mixtures of multiple regressions where the mixing proportions are estimated locally.

Usage

regmixEM.lambda(y, x, lambda = NULL, beta = NULL, sigma = NULL, 
                k = 2, addintercept = TRUE, arbmean = TRUE,
                arbvar = TRUE, epsilon = 1e-8, maxit = 10000,
                verb = FALSE)

Arguments

`y`	An n-vector of response values.
`x`	An nxp matrix of predictors. See `addintercept` below.
`lambda`	An nxk matrix of initial local values of mixing proportions. Entries should sum to 1. This determines number of components. If NULL, then `lambda` is simply one over the number of components.
`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 uniform standard normal entries. If both `lambda` and `beta` are NULL, then number of components is determined by `sigma`.
`sigma`	k-vector of initial global values of standard deviations. If NULL, then 1/\code{sigma}^2 has random standard exponential entries. If `lambda`, `beta`, and `sigma` are NULL, then number of components is determined by `k`.
`k`	The 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.

Details

Primarily used within regmixEM.loc.

Value

regmixEM.lambda 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 inputted 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.

Examples

## Compare a 2-component and 3-component fit to NOdata.

data(NOdata)
attach(NOdata)
set.seed(100)
out1 <- regmixEM.lambda(Equivalence, NO)
out2 <- regmixEM.lambda(Equivalence, NO, k = 3)
c(out1$loglik, out2$loglik)

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