makeDepmix: Dependent Mixture Model Specifiction: full control and adding...

In depmix/depmixS4: Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4

Dependent Mixture Model Specifiction: full control and adding response models

Description

makeDepmix creates an object of class depmix. This function is meant for full control, e.g. specifying each response model and the transition and prior models 'by hand'. For the default easier specification of models, please see depmix. This function is meant for specifying one's own response models.

Usage

	
	makeDepmix(response, transition, prior, ntimes = NULL, homogeneous = TRUE, 
		stationary = NULL, ...) 	
	
	makeMix(response, prior, ...) 	

Arguments

response	A two-dimensional list of response models. See 'Details'.
transition	A list of transition models, each created by a call to transInit. The lenght of this list should be the number of states of the model.
prior	The initial state probabilities model; created through a call to transInit.
ntimes	A vector specifying the lengths of individual, ie independent, time series. If not specified, the responses are assumed to form a single time series.
homogeneous	Logical indicating whether the transition models include time-varying covariates; used internally to determine the dimensions of certain arrays, notably trDens.
stationary	This argument should no longer be used; if not NULL, the value of stationary is copied to the homogeneous argument, with a warning. In future versions this argument may be dropped or used for different purposes, i.e., for specifying models in which the initial state probabilities are constrained to be the stationary distribution of the transition matrix.
...	Not used currently.

Details

The function makeDepmix creates an S4 object of class depmix, which needs to be fitted using fit to optimize the parameters. This function is provided to have full control, eg by specifying one's own response models with distributions that are not provided.

The response model(s) should be created by call(s) to GLMresponse, MVNresponse (see example below) or user-defined response models (see example below) that should extend the response-class and have the following methods: dens, predict and optionally fit. The fit function should have an argument w, providing the weights. If the fit function is not provided, optimization should be done by using Rdonlp (use method="donlp" in calling fit on the depmix model, note that this is not the default). The first index of response models runs over the states of the model, and the second index over the responses to be modeled.

Value

See the depmix help page for the return value, a depmix object.

Author(s)

Ingmar Visser & Maarten Speekenbrink

See Also

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

Examples

# below example recreates the same model as on the fit help page in a roundabout way
# there we had:
# mod1 <- depmix(list(rt~1,corr~1),data=speed,transition=~Pacc,nstates=2,
#	 family=list(gaussian(),multinomial("identity")),ntimes=c(168,134,137))

data(speed)   

rModels <- list(
	list(
		GLMresponse(formula=rt~1,data=speed,family=gaussian()),
		GLMresponse(formula=corr~1,data=speed,family=multinomial("identity"))
	),
	list(
		GLMresponse(formula=rt~1,data=speed,family=gaussian()),
		GLMresponse(formula=corr~1,data=speed,family=multinomial("identity"))
	)
)

transition <- list()
transition[[1]] <- transInit(~Pacc,nstates=2,data=speed)
transition[[2]] <- transInit(~Pacc,nstates=2,data=speed)

inMod <- transInit(~1,ns=2,data=data.frame(rep(1,3)),family=multinomial("identity"))
mod <- makeDepmix(response=rModels,transition=transition,prior=inMod,
ntimes=c(168,134,137),homogeneous=FALSE)

set.seed(3)
fm1 <- fit(mod)
fm1
summary(fm1)


# generate data from two different multivariate normal distributions
m1 <- c(0,1)
sd1 <- matrix(c(1,0.7,.7,1),2,2)
m2 <- c(1,0)
sd2 <- matrix(c(2,.1,.1,1),2,2)
set.seed(2)
y1 <- mvrnorm(50,m1,sd1)
y2 <- mvrnorm(50,m2,sd2)
# this creates data with a single change point
y <- rbind(y1,y2)

# now use makeDepmix to create a depmix model for this bivariate normal timeseries
rModels <-  list()
rModels[[1]] <- list(MVNresponse(y~1))
rModels[[2]] <- list(MVNresponse(y~1))

trstart=c(0.9,0.1,0.1,0.9)

transition <- list()
transition[[1]] <- transInit(~1,nstates=2,data=data.frame(1),pstart=c(trstart[1:2]))
transition[[2]] <- transInit(~1,nstates=2,data=data.frame(1),pstart=c(trstart[3:4]))

instart=runif(2)
inMod <- transInit(~1,ns=2,ps=instart,data=data.frame(1))

mod <- makeDepmix(response=rModels,transition=transition,prior=inMod)

fm2 <- fit(mod,emc=em.control(random=FALSE))

# where is the switch point?
plot(as.ts(posterior(fm2, type="smoothing")[,1]))


# in below example we add the exgaus distribution as a response model and fit
# this instead of the gaussian distribution to the rt slot of the speed data
# most of the actual computations for the exgaus distribution is done by calling
# functions from the gamlss family of packages; see their help pages for 
# interpretation of the mu, nu and sigma parameters that are fitted below

## Not run: 
require(gamlss)
require(gamlss.dist)

data(speed)
rt <- speed$rt

# define a response class which only contains the standard slots, no additional slots
setClass("exgaus", contains="response")

# define a generic for the method defining the response class

setGeneric("exgaus", function(y, pstart = NULL, fixed = NULL, ...) standardGeneric("exgaus"))

# define the method that creates the response class

setMethod("exgaus", 
    signature(y="ANY"), 
    function(y,pstart=NULL,fixed=NULL, ...) {
        y <- matrix(y,length(y))
		x <- matrix(1)
		parameters <- list()
		npar <- 3
		if(is.null(fixed)) fixed <- as.logical(rep(0,npar))
		if(!is.null(pstart)) {
		if(length(pstart)!=npar) stop("length of 'pstart' must be ",npar)
		  parameters$mu <- pstart[1]
		  parameters$sigma <- log(pstart[2])
		  parameters$nu <- log(pstart[3])
        }
        mod <- new("exgaus",parameters=parameters,fixed=fixed,x=x,y=y,npar=npar)
        mod
	}
)

setMethod("show","exgaus",
    function(object) {
        cat("Model of type exgaus (see ?gamlss for details) \n")
        cat("Parameters: \n")
        cat("mu: ", object@parameters$mu, "\n")
        cat("sigma: ", object@parameters$sigma, "\n")
        cat("nu: ", object@parameters$nu, "\n")
    }
)

setMethod("dens","exgaus",
 function(object,log=FALSE) {
   dexGAUS(object@y, mu = predict(object), 
		sigma = exp(object@parameters$sigma), nu = exp(object@parameters$nu), log = log)
  }
)

setMethod("getpars","response",
    function(object,which="pars",...) {
        switch(which,
            "pars" = {
                parameters <- numeric()
                parameters <- unlist(object@parameters)
                pars <- parameters
            },
            "fixed" = {
                pars <- object@fixed
            }
        )
        return(pars)
    }
)

setMethod("setpars","exgaus",
    function(object, values, which="pars", ...) {
        npar <- npar(object)
        if(length(values)!=npar) stop("length of 'values' must be",npar)
        # determine whether parameters or fixed constraints are being set
		nms <- names(object@parameters)
		switch(which,
		  "pars"= {
		      object@parameters$mu <- values[1]
		      object@parameters$sigma <- values[2]
		      object@parameters$nu <- values[3]
		      },
		  "fixed" = {
		      object@fixed <- as.logical(values)
		  }
		  )
        names(object@parameters) <- nms
        return(object)
    }
)

setMethod("fit","exgaus",
    function(object,w) {
        if(missing(w)) w <- NULL
        y <- object@y
        fit <- gamlss(y~1,weights=w,family=exGAUS(),
			control=gamlss.control(n.cyc=100,trace=FALSE),
			mu.start=object@parameters$mu,
			sigma.start=exp(object@parameters$sigma),
			nu.start=exp(object@parameters$nu))
		pars <- c(fit$mu.coefficients,fit$sigma.coefficients,fit$nu.coefficients)
		object <- setpars(object,pars)
		object
	}
)

setMethod("predict","exgaus", 
    function(object) {
        ret <- object@parameters$mu
        return(ret)
    }
)

rModels <- list(
  list(
	  exgaus(rt,pstart=c(5,.1,.1)),
		GLMresponse(formula=corr~1, data=speed, 
		family=multinomial("identity"), pstart=c(0.5,0.5))
	),
	list(
		exgaus(rt,pstart=c(6,.1,.1)),
		GLMresponse(formula=corr~1, data=speed, 
		family=multinomial("identity"), pstart=c(.1,.9))
	)
)

trstart=c(0.9,0.1,0.1,0.9)
transition <- list()
transition[[1]] <- transInit(~Pacc,nstates=2,data=speed,pstart=c(trstart[1:2],0,0))
transition[[2]] <- transInit(~Pacc,nstates=2,data=speed,pstart=c(trstart[3:4],0,0))

instart=c(0.5,0.5)
inMod <- transInit(~1,ns=2,ps=instart,family=multinomial("identity"), data=data.frame(rep(1,3)))

mod <- makeDepmix(response=rModels,transition=transition,prior=inMod,ntimes=c(168,134,137), 
homogeneous=FALSE)

fm3 <- fit(mod,emc=em.control(rand=FALSE))
summary(fm3)

## End(Not run)

depmix/depmixS4 documentation built on May 31, 2024, 8:09 a.m.