\name{responses} \alias{responses} \alias{BINOMresponse} \alias{GAMMAresponse} \alias{MULTINOMresponse} \alias{MVNresponse} \alias{NORMresponse} \alias{POISSONresponse} \alias{show,MVNresponse-method} \title{Response models currently implemented in depmix.} \description{Depmix contains a number of default response models. We provide a brief description of these here.} \section{BINOMresponse}{ \code{BINOMresponse} is a binomial response model. It derives from the basic \code{\link{GLMresponse}} class. \describe{ \item{y:}{The dependent variable can be either a binary vector, a factor, or a 2-column matrix, with successes and misses.} \item{x:}{The design matrix.} \item{Parameters:}{A named list with a single element coefficients'', which contains the GLM coefficients.} } } \section{GAMMAresponse}{ \code{GAMMAresponse} is a model for a Gamma distributed response. It extends the basic \code{\link{GLMresponse}} class directly. \describe{ \item{y:}{The dependent variable.} \item{x:}{The design matrix.} \item{Parameters:}{A named list with a single element coefficients'', which contains the GLM coefficients.} } } \section{MULTINOMresponse}{ \code{MULTINOMresponse} is a model for a multinomial distributed response. It extends the basic \code{\link{GLMresponse}} class, although the functionality is somewhat different from other models that do so. \describe{ \item{y:}{The dependent variable. This is a binary matrix with N rows and Y columns, where Y is the total number of categories.} \item{x:}{The design matrix.} \item{Parameters:}{A named list with a single element coefficients'', which is an \code{ncol(x)} by \code{ncol(y)} matrix which contains the GLM coefficients.} } } \section{MVNresponse}{ \code{MVNresponse} is a model for a multivariate normal distributed response. \describe{ \item{y:}{The dependent variable. This is a matrix.} \item{x:}{The design matrix.} \item{Parameters:}{A named list with a elements coefficients'', which contains the GLM coefficients, and Sigma'', which contains the covariance matrix.} } } \section{NORMresponse}{ \code{NORMresponse} is a model for a normal (Gaussian) distributed response. It extends the basic \code{\link{GLMresponse}} class directly. \describe{ \item{y:}{The dependent variable.} \item{x:}{The design matrix.} \item{Parameters:}{A named list with elements coefficients'', which contains the GLM coefficients, and sd'', which contains the standard deviation.} } } \section{POISSONresponse}{ \code{POISSONresponse} is a model for a Poisson distributed response. It extends the basic \code{\link{GLMresponse}} class directly. \describe{ \item{y:}{The dependent variable.} \item{x:}{The design matrix.} \item{Parameters:}{A named list with a single element coefficients'', which contains the GLM coefficients.} } } \section{examples}{ mod <- GLMresponse(rnorm(1000)~1) fit(mod) mod <- GLMresponse(sample(1:3,1000,rep=TRUE)~1,family=multinomial()) fit(mod) colSums(mod@y)/1000 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)) 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) x <- abs(rnorm(1000,2)) res <- rpois(1000,x) mod <- GLMresponse(res~x,family=poisson()) fit(mod) glm(res~x, family=poisson) 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) 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) } \author{Maarten Speekenbrink} \keyword{models}