mix: Mixture Model Specifiction
In koooee/depmixS4: Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4

Description Usage Arguments Details Value Author(s) References See Also Examples

mix creates an object of class mix, an (independent) mixture model (as a limit case of dependent mixture models in which all observed time series are of length 1), otherwise known latent class or mixture model. For a short description of the package see depmixS4.

	
	mix(response, data=NULL, nstates, family=gaussian(), 
		prior=~1, initdata=NULL, respstart=NULL, instart=NULL,...)

`response`	The response to be modeled; either a formula or a list of formulae in the multivariate case; this interfaces to the glm distributions. See 'Details'.
`data`	An optional data.frame to interpret the variables in the response and transition arguments.
`nstates`	The number of states of the model.
`family`	A family argument for the response. This must be a list of family's if the response is multivariate.
`prior`	A one-sided formula specifying the density for the prior or initial state probabilities.
`initdata`	An optional data.frame to interpret the variables occuring in prior. The number of rows of this data.frame must be equal to the number of cases being modeled. See 'Details'.
`respstart`	Starting values for the parameters of the response models.
`instart`	Starting values for the parameters of the prior or initial state probability model.
`...`	Not used currently.

The function mix creates an S4 object of class mix, which needs to be fitted using fit to optimize the parameters.

The response model(s) are by default created by call(s) to GLMresponse using the formula and the family arguments, the latter specifying the error distribution. See GLMresponse for possible values of the family argument for glm-type responses (ie a subset of the glm family options, and the multinomial). Alternative response distributions are specified by using the makeDepmix function. Its help page has examples of specifying a model with a multivariate normal response, as well as an example of adding a user-defined response model, in this case for the ex-gauss distribution.

If response is a list of formulae, the response's are assumed to be independent conditional on the latent state.

The prior density is modeled as a multinomial logistic. This model is created by a call to transInit.

Starting values may be provided by the respective arguments. The order in which parameters must be provided can be easily studied by using the setpars and getpars functions.

Linear constraints on parameters can be provided as argument to the fit function.

The print function prints the formulae for the response and prior models along with their parameter values.

mix returns an object of class mix which has the following slots:

`response`	A list of a list of response models; the first index runs over states; the second index runs over the independent responses in case a multivariate response is provided.
`prior`	A multinomial logistic model for the initial state probabilities.
`dens,init`	See `mix-class` help for details. For internal use.
`ntimes`	A vector made by `rep(1,nrow(data))`; for internal use only.
`nstates`	The number of states of the model.
`nresp`	The number of independent responses.
`npars`	The total number of parameters of the model. Note: this is not the degrees of freedom because there are redundancies in the parameters, in particular in the multinomial models for the transitions and prior probabilities.

Ingmar Visser

A. L. McCutcheon (1987). Latent class analysis. Sage Publications.

fit, transInit, GLMresponse, depmix-methods for accessor functions to depmix objects.

# four binary items on the balance scale task
data(balance)

# define a latent class model
instart=c(0.5,0.5)
set.seed(1)
respstart=runif(16)
# note that ntimes argument is used to make this a mixture model
mod <- mix(list(d1~1,d2~1,d3~1,d4~1), data=balance, nstates=2,
	family=list(multinomial(),multinomial(),multinomial(),multinomial()),
	respstart=respstart,instart=instart)
# to see the model
mod