Estimates the parameters for a mixture of linear regressions, assuming Gaussian errors, using the EM algorithm.
mixreg(x, y, ncomp = 2, intercept = TRUE, eq.var = FALSE,
theta.start = NULL, itmax = 1000, eps = 1e-06, verb = TRUE,
digits = 7, max.try = 5, data.name = NULL)A matrix of predictors for each of the regression models in the mixture. It should NOT include an initial column of 1s. If there is only one predictor, x may be a vector.
The vector of responses for the regression models.
The number of components in the mixture.
Logical argument specifying whether the linear regressions should have intercepts fitted.
Logical argument specifying whether the error variance should be the same for all components, or each component should be allowed a different error variance.
A list giving starting values for the estimation procedure. Each component of the list is in turn a list with components beta (vector of linear coefficients), sigsq (variance) and lambda (mixing probability). If eq.var is TRUE, then it is sensible to have all the starting values of sigsq equal, but this is not strictly necessary. If theta.start is not specified, starting values are generated randomly. This is NOT recommended.
A character string specifying a name associated with the data being analyzed, for identification purposes.
The maximum number of EM steps to be undertaken.
A value specifying the convergence criterion for the EM algorithm. If the maximum absolute value of the change in the parameters is less than eps the algorithm is considered to have converged.
Logical argument; if verb is TRUE then details of the progress of the algorithm are printed out at each EM step.
The number of digits to which the details are printed out, when verb is TRUE.
If the algorithm encounters a singularity in the likelihood (as may occur when eq.var is FALSE) the algorithm is restarted using new (randomly generated) starting values. The restart is attempted a maximum of max.try times.
A list, of class mixreg, with components
The parameters of the fitted model arranged as a matrix, each row corresponding to one component of the mixture.
The parameters of the fitted model as a list, each entry of the list being itself a list (like those in theta.start) corresponding to one component of the mixture.
The log likelihood of the fitted model, based on Gaussian errors.
The Akaike Information Criterion value for the fitted model; aic is equal to -2 * log.like + 2*M where M is the number of parameters in the model.
The intercept argument of the call to mixreg.
The eq.var argument of the call to mixreg.
A vector of names associated with the linear components of the regression models. The names are formed from the column names of the argument x if these exist; otherwise they are "beta1", "beta2", …. The name "Int" is prepended if intecept is TRUE.
The number of steps the EM algorithm took to converge.
Logical value indicating whether the algorithm did converge or stopped because it reach the itmax EM step.
The data.name argument if supplied; otherwise is formed as "name-of-y.on.name-of-x".
Even if eq.var is TRUE, each component of theta still has its own sigsq component. The values of these will all be equal however if eq.var is TRUE.
Turner, T. R. (2000) Estimating the rate of spread of a viral infection of potato plants via mixtures of regressions. Appl. Statist. vol. 49, Part 3, pp. 371 -- 384.
Dempster, A. P., Laird, N. M., and Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm, J. Royal Statist. Soc. B, vol. 39, pp. 1--22, 1977.
bootcomp, cband, covmix, plot.cband, plot.mresid, qqMix, residMix
# NOT RUN {
data(aphids)
x <- aphids$n.aphids
y <- aphids$n.inf
TS <- list(list(beta=c(3.0,0.1),sigsq=16,lambda=0.5),
list(beta=c(0.0,0.0),sigsq=16,lambda=0.5))
fit <- mixreg(x,y,ncomp=2,theta.start=TS,data.name='aphids')
cvm <- covmix(fit,x,y)
cbd <- cband(fit,cvm,x,y)
plot(cbd)
r <- residMix(fit,x,y)
plot(r)
r <- residMix(fit,x,y,std=TRUE)
qqMix(r)
# }
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