depmixS4 (version 0.2-1)

responses: Response models currently implemented in depmix.

Description

Depmix contains a number of default response models. We provide a brief description of these here.

Examples

# binomial response model
	x <- rnorm(1000)
	library(boot)
	p <- inv.logit(x)
	ss <- rbinom(1000,1,p)
	mod <- GLMresponse(cbind(ss,1-ss)~x,family=binomial())
	fit(mod)
	glm(cbind(ss,1-ss)~x, family=binomial)
	
	# gamma response model
	x=runif(1000,1,5)
	res <- rgamma(1000,x)
	## note that gamma needs proper starting values which are not
	## provided by depmixS4 (even with them, this may produce warnings)
	mod <- GLMresponse(res~x,family=Gamma(),pst=c(0.8,1/0.8))
	fit(mod)
	glm(res~x,family=Gamma)
	
	# multinomial response model
	x <- sample(0:1,1000,rep=TRUE)
	mod <- GLMresponse(sample(1:3,1000,rep=TRUE)~x,family=multinomial(),pstart=c(0.33,0.33,0.33,0,0,1))
	mod@y <- simulate(mod)
	fit(mod)
	colSums(mod@y[which(x==0),])/length(which(x==0))
	colSums(mod@y[which(x==1),])/length(which(x==1))
	
	# multivariate normal response model
	mn <- c(1,2,3)
	sig <- matrix(c(1,.5,0,.5,1,0,0,0,2),3,3)
	y <- mvrnorm(1000,mn,sig)
	mod <- MVNresponse(y~1)
	fit(mod)
	colMeans(y)
	var(y)
	
	# normal (gaussian) response model
	y <- rnorm(1000)
	mod <- GLMresponse(y~1)
	fm <- fit(mod)
	cat("Test gaussian fit: ", all.equal(getpars(fm),c(mean(y),sd(y)),check=FALSE))
	
	
	# poisson response model
	x <- abs(rnorm(1000,2))
	res <- rpois(1000,x)
	mod <- GLMresponse(res~x,family=poisson())
	fit(mod)
	glm(res~x, family=poisson)