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mixtools (version 1.0.1)

poisregmixEM: EM Algorithm for Mixtures of Poisson Regressions

Description

Returns EM algorithm output for mixtures of Poisson regressions with arbitrarily many components.

Usage

poisregmixEM(y, x, lambda = NULL, beta = NULL, k = 2,
             addintercept = TRUE, epsilon = 1e-08, 
             maxit = 10000, verb = FALSE)

Arguments

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 is generated by binning the data into k bins and using glm
k
Number of components. Ignored unless lambda and beta are both NULL.
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.

Value

  • poisregmixEM returns a list of class mixEM with items:
  • xThe predictor values.
  • yThe response values.
  • lambdaThe final mixing proportions.
  • betaThe final Poisson regression coefficients.
  • loglikThe final log-likelihood.
  • posteriorAn nxk matrix of posterior probabilities for observations.
  • all.loglikA vector of each iteration's log-likelihood.
  • restartsThe number of times the algorithm restarted due to unacceptable choice of initial values.
  • ftA character vector giving the name of the function.

References

McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models, John Wiley & Sons, Inc. Wang, P., Puterman, M. L., Cockburn, I. and Le, N. (1996) Mixed Poisson Regression Models with Covariate Dependent Rates, Biometrics, 52(2), 381--400.

See Also

logisregmixEM

Examples

Run this code
## EM output for data generated from a 2-component model.

set.seed(100)
beta <- matrix(c(1, .5, .7, -.8), 2, 2)
x <- runif(50, 0, 10)
xbeta <- cbind(1, x)%*%beta
w <- rbinom(50, 1, .5)
y <- w*rpois(50, exp(xbeta[, 1]))+(1-w)*rpois(50, exp(xbeta[, 2]))
out <- poisregmixEM(y, x, verb = TRUE,  epsilon = 1e-03)
out

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