regmixEM.chgpt {mixtools} | R Documentation |
EM Algorithm for Mixtures of Regressions with a Changepoint
Description
Returns EM algorithm output for a mixture of a regression with a changepoint and a simple
linear regression.
Usage
regmixEM.chgpt(y, x, lambda = NULL, gamma = NULL, beta = NULL,
sigma = NULL, k = 2, T = 3, t = NULL,
epsilon = 1e-08, maxit = 10000, verb = FALSE)
Arguments
y |
An n-vector of response values. |
x |
An n-vector of predictor values. A column of ones is automatically appended to x. |
lambda |
Initial value of mixing proportions. Entries should sum to
1. If NULL, then lambda is
random from uniform Dirichlet. |
gamma |
Initial value of gamma parameters for the changepoint component. Should be a 3-dimenstional vector.
If NULL, then gamma has standard normal entries according to a binning method done on the data. |
beta |
Initial value of beta parameters for the simple linear regression component.
Should be a 2-dimenstional vector. 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. |
k |
Number of components. Currently, this value must be set equal to 2. |
T |
The number of values to leave off for the range of all possible changepoints to be tested. |
t |
Initial value of the changepoint to consider. |
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
regmixEM.chgpt
returns a list of class mixEM
with items:
lambda |
The final mixing proportions. |
gamma |
The final regression coefficients for the changepoint component. |
beta |
The final regression coefficients for the simple linear regression component. |
sigma |
The final standard deviations. |
cutpoint |
The estimated value for the changepoint in the changepoint component. |
loglik |
The final log-likelihood. |
posterior |
A nx2 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. |
See Also
regmixEM
Examples
## EM output for simulated data.
w<-rbinom(100, 1, .5)
cpt<-50
x<-sort(runif(100, 0, 10))
x1<-cbind(1, x)
xt<-cbind(x1, (x-x[cpt])*(x>x[cpt]))
beta<-c(5, -1)
gamma<-c(15, -1, 2)
y<-w*rnorm(100, mean = xt%*%gamma, sd = .1) +
(1-w)*rnorm(100, mean = x1%*%beta, sd = .1)
out<-regmixEM.chgpt(y = y, x = x, t = cpt, beta = beta,
gamma = gamma, verb = TRUE, epsilon = 1e-03)
out
[Package
mixtools version 0.3.3
Index]