logisregmixEM {mixtools}R Documentation

EM Algorithm for Mixtures of (Binary) Logistic or Binomial Regressions

Description

Returns EM algorithm output for mixtures of logistic or binomial regressions with arbitrarily many components.

Usage

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

Arguments

y An n-vector of successes out of N trials.
x An nxp matrix of predictors. See addintercept below.
N An n-vector of number of trials for binomial regression. If NULL, then N is an n-vector of 1s for logistic regression.
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 on the values in each of the bins. If both lambda and beta are NULL, then number of components is determined by k.
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

logisregmixEM returns a list of class mixEM with items:

x The predictor values.
y The response values.
lambda The final mixing proportions.
beta The final logistic regression coefficients.
loglik The final log-likelihood.
posterior An nxk matrix of posterior probabilities for observations.
all.loglik A vector of each iteration's log-likelihood.
ft A character vector giving the name of the function.

References

McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models, John Wiley & Sons, Inc.

See Also

poisregmixEM

Examples

## EM output for data generated from a 2-component binomial regression model.

beta<-matrix(c(1, .5, 2, -.8), 2, 2)
x<-runif(50, 0, 10)
x1<-cbind(1, x)
xbeta<-x1%*%beta
N<-ceiling(runif(50, 50, 75))
w<-rbinom(50, 1, .3)
y<-w*rbinom(50, size = N, prob = (1/(1+exp(-xbeta[, 1]))))+
           (1-w)*rbinom(50, size = N, prob = 
           (1/(1+exp(-xbeta[, 2]))))
out.1<-logisregmixEM(y, x, N, verb = TRUE)
out.1

## EM output for data generated from a 2-component logistic regression model.

beta<-matrix(c(-10, .1, 20, -.1), 2, 2)
x<-runif(500, 50, 250)
x1<-cbind(1, x)
xbeta<-x1%*%beta
w<-rbinom(500, 1, .3)
y<-w*rbinom(500, size = 1, prob = (1/(1+exp(-xbeta[, 1]))))+
            (1-w)*rbinom(500, size = 1, prob = 
            (1/(1+exp(-xbeta[, 2]))))
out.2<-logisregmixEM(y, x, beta = beta, lambda = c(.3, .7), 
                     verb = TRUE, epsilon = 1e-5)
out.2

[Package mixtools version 0.1.0 Index]