Description Usage Arguments Value Note References Examples
Estimates parameters of a finite mixture of univariate normals by the method of penalized maximum likelhood. Using this function is equivalent to calling normalmixPMLE with regressors specified by x as a parameter.
1 2 3 | regmixPMLE(y, x, m = 2, z = NULL, vcov.method = c("Hessian", "OPG",
"none"), ninits = 10, epsilon = 1e-08, maxit = 2000,
epsilon.short = 0.01, maxit.short = 500, binit = NULL)
|
y |
n by 1 vector of data for y |
x |
n by q matrix of data for x |
m |
The number of components in the mixture |
z |
n by p matrix of regressor associated with gam |
vcov.method |
Method used to compute the variance-covariance matrix, one of |
ninits |
The number of randomly drawn initial values. |
epsilon |
The convergence criterion. Convergence is declared when the penalized log-likelihood increases by less than |
maxit |
The maximum number of iterations. |
epsilon.short |
The convergence criterion in short EM. Convergence is declared when the penalized log-likelihood increases by less than |
maxit.short |
The maximum number of iterations in short EM. |
binit |
The initial value of parameter vector that is included as a candidate parameter vector |
A list of class normalMix
with items:
coefficients |
A vector of parameter estimates. Ordered as α_1,…,α_m,μ_1,…,μ_m,σ_1,…,σ_m,\gam. |
parlist |
The parameter estimates as a list containing alpha, mu, and sigma (and gam if z is included in the model). |
vcov |
The estimated variance-covariance matrix. |
loglik |
The maximized value of the log-likelihood. |
penloglik |
The maximized value of the penalized log-likelihood. |
aic |
Akaike Information Criterion of the fitted model. |
bic |
Bayesian Information Criterion of the fitted model. |
postprobs |
n by m matrix of posterior probabilities for observations |
components |
n by 1 vector of integers that indicates the indices of components each observation belongs to based on computed posterior probabilities |
call |
The matched call. |
m |
The number of components in the mixture. |
regmixPMLE
maximizes the penalized log-likelihood function
using the EM algorithm with combining short and long runs of EM steps as in Biernacki et al. (2003).
regmixPMLE
first runs the EM algorithm from ninits
* 4m(1 + p) initial values
with the convertence criterion epsilon.short
and maxit.short
.
Then, regmixPMLE
uses ninits
best initial values to run the EM algorithm
with the convertence criterion epsilon
and maxit
.
Biernacki, C., Celeux, G. and Govaert, G. (2003) Choosing Starting Values for the EM Algorithm for Getting the Highest Likelihood in Multivariate Gaussian Mixture Models, Computational Statistics and Data Analysis, 41, 561–575.
Boldea, O. and Magnus, J. R. (2009) Maximum Likelihood Estimation of the Multivariate Normal Mixture Model, Journal of the American Statistical Association, 104, 1539–1549.
Chen, J., Tan, X. and Zhang, R. (2008) Inference for Normal Mixtures in Mean and Variance, Statistica Sinica, 18, 443–465.
McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models, John Wiley \& Sons, Inc.
1 2 3 4 | data(faithful)
attach(faithful)
regmixPMLE(y = eruptions, x = waiting, m = 1)
regmixPMLE(y = eruptions, x = waiting, m = 2)
|
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