regmixEM.loc {mixtools} | R Documentation |
Iterative Algorithm Using EM Algorithm for Mixtures of Regressions with
Local Lambda Estimates
Description
Iterative algorithm returning EM algorithm output for mixtures of multiple regressions where the mixing proportions
are estimated locally.
Usage
regmixEM.loc(y, x, lambda = NULL, beta = NULL, sigma = NULL,
k = 2, addintercept = TRUE, kern.l = c("Gaussian",
"Beta", "Triangle", "Cosinus", "Optcosinus"),
epsilon = 1e-08, maxit = 10000, kernl.g = 0,
kernl.h = 1, 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 global values 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 |
A k-vector of initial global values of standard deviations.
If NULL, then 1/sigma ^2 has random standard exponential entries.
If lambda , beta , and sigma are NULL, then number of components 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. |
kern.l |
The type of kernel to use in the local estimation of lambda . |
epsilon |
The convergence criterion. |
maxit |
The maximum number of iterations. |
kernl.g |
A shape parameter required for the symmetric beta kernel for local estimation of lambda .
The default is g = 0 which yields the uniform kernel. Some common values are g = 1 for the
Epanechnikov kernel, g = 2 for the biweight kernel, and g = 3 for the triweight kernel. |
kernl.h |
The bandwidth controlling the size of the window used in the
local estimation of lambda around x. |
verb |
If TRUE, then various updates are printed during each iteration of the algorithm. |
Value
regmixEM.loc
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.x |
The final local mixing proportions. |
beta |
The final global regression coefficients. |
sigma |
The final global standard deviations. |
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. |
See Also
regmixEM.lambda
Examples
## Compare a 2-component and 3-component fit to NOdata.
data(NOdata)
attach(NOdata)
out1<-regmixEM.loc(Equivalence, NO, kernl.h = 2,
epsilon = 1e-02, verb = TRUE)
out2<-regmixEM.loc(Equivalence, NO, kernl.h = 2, k = 3,
epsilon = 1e-02, verb = TRUE)
c(out1$loglik, out2$loglik)
[Package
mixtools version 0.3.3
Index]