flexmixNL-package {flexmixNL}

Mixtures of Generalized Nonlinear Models

Description

Extension of package flexmix for fitting mixtures of Generalized Nonlinear Models.

Details

The package flexmixNL implements an extension for the package flexmix for fitting mixtures of Generalized Nonlinear Models (GNMs). The package provides a specified M-step for the EM-algorithm within the FlexMix framework (see also flexmix) for fitting GNMs for the normal and gamma distribution. The mixture model is specified by the function FLXMRnlm.

Author(s)

Sanela Omerovic

See Also

See also flexmix for finite mixtures of regression models and gnm for the fitting of Generalized Nonlinear Models (GNMs) for further information.

Examples

# example 1.
data("NReg", package = "flexmixNL")
# mixture of two nonlinear regression models (normal distribution).
start1 <- list(a = 170, b = 5)
start2 <- list(a = 130, b = 5)
model <- flexmix(yn ~ x, k = 2, data = NReg,
                     model = list(FLXMRnlm(formula = yn ~ a*x / (b+x),
                                           family = "gaussian", 
                                           start = list(start1, start2))))
# final cluster assignments.                                           
plot(yn ~ x, col = clusters(model), data = NReg)

# example 2.
data("GReg", package = "flexmixNL")
# mixture of two nonlinear regression models (gamma distribution).
exp.1 = function(x,predictors){
  list(predictors = list(a = 1, b = 1),
       variables = list(substitute(x)),
       term = function(predictors, variables){
         sprintf("exp( %s + %s * %s)", 
         predictors[1], predictors[2], variables)
       })
}
class(exp.1) = "nonlin"

start1 <- list(a = -0.4, b = 0.3)
start2 <- list(a = -0.1, b = 0.4)
model2 <- flexmix(yg ~ x, k = 2, data = GReg,
                      model = list(FLXMRnlm(formula = yg ~ -1 + exp.1(x),
                                            family = "Gamma", 
                                            start = list(start1, start2))))
# final cluster assignments.  
plot(yg ~ x, col = clusters(model2), data = GReg)