regmixMH {mixtools} | R Documentation |
Return Metropolis-Hastings (M-H) algorithm output for mixtures of multiple regressions with arbitrarily many components.
regmixMH(y, x, lambda = NULL, beta = NULL, s = NULL, k = 2, addintercept = TRUE, mu = NULL, sig = NULL, sampsize = 1000, omega = .01, thin = 1)
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 uniform standard normal entries. If both
lambda and beta are NULL, then number of components is determined by s . |
s |
k-vector of standard deviations. If NULL, then 1/s ^2 has
random standard exponential entries. If lambda , beta , and s are
NULL, then number of components determined by k . |
k |
Number of components. Ignored unless all of lambda , beta ,
and s are NULL. |
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 beta ;
the means of beta components. If NULL,
these are set to zero. |
sig |
The prior hyperparameter of same size as beta ;
the standard deviations of beta components. If NULL, these are
all set to five times the overall standard deviation of y. |
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. |
regmixMH
returns a list of class mixMCMC
with items:
x |
A nxp matrix of the predictors. |
y |
A vector of the responses. |
theta |
A (sampsize /thin ) x q matrix of MCMC-sampled
q-vectors, where q is the total number of parameters in beta , s , and
lambda . |
k |
The number of components. |
Hurn, M., Justel, A. and Robert, C. P. (2003) Estimating Mixtures of Regressions, Journal of Computational and Graphical Statistics 12(1), 55–79.
## M-H algorithm for NOdata with acceptance rate about 40%. data(NOdata) attach(NOdata) 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,]