Fitting Unregularized foehnix Mixture Model With Concomitant Model.

Fitting method for two-component foehnix mixture model with additional concomitants. Typically not called directly, interfaced via foehnix.

foehnix.unreg.fit(
  y,
  logitX,
  family,
  switch = FALSE,
  maxit = 100L,
  tol = 1e-05,
  verbose = TRUE,
  ...
)

Arguments

y: numeric vector, covariate for the components of the mixture model, dimension N.
logitX: numeric matrix of dimension N x P, covariates for the concomitant model (logistic regression model matrix).
family: object of class foehnix.family.
switch: logical whether or not the two components should be switched. By default (switch = FALSE) the component which shows higher values of y is assumed to be the foehn cluster! Depending on what your covariate is you might need to switch the clusters (by setting switch = TRUE).
maxit: positive integer, or vector of length 2 with positive integer values. Maximum number of iterations of the EM algorithm and the concomitant model. Check manual of foehnix.control for more details.
tol: numeric, or vector of length 2 containing numeric values. Tolerance for the optimization (EM algorithm and concomitant model). Check manual of foehnix.control for more details.
verbose: logical, default TRUE. If set to FALSE verbose output will be suppressed.
...: currently unused.

Reto Stauffer