regmixMH {mixtools} | R Documentation |
Metropolis-Hastings Algorithm for Mixtures of Regressions
Description
Return Metropolis-Hastings (M-H) algorithm output for mixtures of multiple regressions with arbitrarily many components.
Usage
regmixMH(y, x, lambda = NULL, beta = NULL, s = NULL, k = 2,
addintercept = TRUE, mu = NULL, sig = NULL, lam.hyp = NULL,
sampsize = 1000, omega = 0.01, thin = 1)
Arguments
y |
An n-vector of response values. |
x |
An nxp matrix of predictors. See |
lambda |
Initial value of mixing proportions. Entries should sum to
1. This determines number of components. If NULL, then |
beta |
Initial value of |
s |
k-vector of standard deviations. If NULL, then |
k |
Number of components. Ignored unless all of |
addintercept |
If TRUE, a column of ones is appended to the x matrix before the value of p is calculated. |
mu |
The prior hyperparameter of same size as |
sig |
The prior hyperparameter of same size as |
lam.hyp |
The prior hyperparameter of length |
sampsize |
Size of posterior sample returned. |
omega |
Multiplier of step size to control M-H acceptance rate. Values closer to zero result in higher acceptance rates, generally. |
thin |
Lag between parameter vectors that will be kept. |
Value
regmixMH
returns a list of class mixMCMC
with items:
x |
A nxp matrix of the predictors. |
y |
A vector of the responses. |
theta |
A ( |
k |
The number of components. |
References
Hurn, M., Justel, A. and Robert, C. P. (2003) Estimating Mixtures of Regressions, Journal of Computational and Graphical Statistics 12(1), 55–79.
See Also
Examples
## M-H algorithm for NOdata with acceptance rate about 40%.
data(NOdata)
attach(NOdata)
set.seed(100)
beta <- matrix(c(1.3, -0.1, 0.6, 0.1), 2, 2)
sigma <- c(.02, .05)
MH.out <- regmixMH(Equivalence, NO, beta = beta, s = sigma,
sampsize = 2500, omega = .0013)
MH.out$theta[2400:2499,]