hmeEM {mixtools} | R Documentation |
Returns EM algorithm output for a mixture-of-experts model. Currently, this code only handles a 2-component mixture-of-experts, but will be extended to the general k-component hierarchical mixture-of-experts.
hmeEM(y, x, lambda = NULL, beta = NULL, sigma = NULL, w = NULL, k = 2, addintercept = TRUE, epsilon = 1e-08, maxit = 10000, verb = FALSE)
y |
An n-vector of response values. |
x |
An nxp matrix of predictors. See addintercept below. |
lambda |
Initial value of mixing proportions, which are modeled as an inverse
logit function of the predictors. Entries should sum to 1.
If NULL, then lambda is taken as 1/k for each x . |
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. |
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. |
w |
A p-vector of coefficients for the way the mixing proportions are modeled. See lambda . |
k |
Number of components. Currently, only k =2 is accepted. |
addintercept |
If TRUE, a column of ones is appended to the x matrix before the value of p is calculated. |
epsilon |
The convergence criterion. |
maxit |
The maximum number of iterations. |
verb |
If TRUE, then various updates are printed during each iteration of the algorithm. |
hmeEM
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. |
w |
The final coefficients for the functional form of the mixing proportions. |
lambda |
An nxk matrix of 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. |
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. |
Jacobs, R. A., Jordan, M. I., Nowlan, S. J. and Hinton, G. E. (1991) Adaptive Mixtures of Local Experts, Neural Computation 3(1), 79–87.
McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models, John Wiley & Sons, Inc.
## EM output for NOdata. data(NOdata) attach(NOdata) em.out<-regmixEM(Equivalence, NO) hme.out<-hmeEM(Equivalence, NO, beta = em.out$beta) hme.out[3:7]