R/mixture.R

Defines functions coef.lvm.mixture plot.lvm.mixture print.lvm.mixture print.summary.lvm.mixture summary.lvm.mixture vcov.lvm.mixture logLik.lvm.mixture information.lvm.mixture score.lvm.mixture ll predict.lvm.mixture manifest.lvm.mixture iid.lvm.mixture model.frame.lvm.mixture mixture frobnorm

Documented in mixture

frobnorm <- function(x,y=0,...) {
    sum((x-y)^2)^.5
}

###{{{ mixture

mixture <- function(x, data, k=length(x), control, FUN, type=c("standard","CEM","SEM"),...) {    

  optim <- list(start=NULL,
                startbounds=c(-2,2),
                startmean=FALSE,
                nstart=1,
                prob=NULL,
                iter.EM=5,
                iter.max=500,
                delta=1e-2,
                stabil=TRUE,
                gamma=1,
                gamma2=1,
                newton=20,
                tol=1e-6,
                ltol=NULL,
                method="scoring",
                constrain=TRUE,
                stopc=2,
                lbound=1e-9,
                trace=1,
                lambda=0 # Stabilizing factor (avoid singularities of I)
                )
  
  type <- tolower(type[1])
  if (!missing(control))
    optim[names(control)] <- control
  if (is.null(optim$ltol)) optim$ltol <- optim$tol
  if (k==1) {
    if (is.list(x))
      res <- estimate(x[[1]],data,...)
    else
      res <- estimate(x,data,...)
    return(res)
  }
  if (class(x)[1]=="lvm") {
    index(x) <- reindex(x,zeroones=TRUE,deriv=TRUE)
    x <- rep(list(x),k)
  }  

  mg <- multigroup(x,rep(list(data),k),fix=FALSE)
  ## Bounds on variance parameters
  npar <- with(mg, npar+npar.mean)
  parpos <- modelPar(mg,1:npar)$p  
  lower <- rep(-Inf, mg$npar);
  offdiagpos <- c()
  varpos <- c()
  for (i in 1:k) {
    vpos <- sapply(mg$parlist[[i]][variances(mg$lvm[[i]])], function(y) as.numeric(substr(y,2,nchar(y))))
    offpos <- sapply(mg$parlist[[i]][offdiags(mg$lvm[[i]])], function(y) as.numeric(substr(y,2,nchar(y))))
    varpos <- c(varpos, vpos)
    offdiagpos <- c(offdiagpos,offpos)
    if (length(vpos)>0)
      lower[vpos] <- optim$lbound  ## Setup optimization constraints
  }
  lower <- c(rep(-Inf,mg$npar.mean), lower)
  constrained <- which(is.finite(lower))
  if (!any(constrained)) optim$constrain <- FALSE

  mymodel <- list(multigroup=mg,k=k,data=data); class(mymodel) <- "lvm.mixture"

  if (is.null(optim$start)) {
    constrLogLikS <- function(p) {      
      if (optim$constrain) {
        p[constrained] <- exp(p[constrained])
      }
      -logLik(mymodel,p=p,rep(1/k,k))
    }

    start <- runif(npar,optim$startbounds[1],optim$startbounds[2]);
    if (length(offdiagpos)>0)
      start[mg$npar.mean + offdiagpos] <- 0
    if (optim$nstart>1) {
      myll <- constrLogLikS(start)
      for (i in 1:optim$nstart) {
        newstart <- runif(npar,optim$startbounds[1],optim$startbounds[2]);
        newmyll <- constrLogLikS(newstart)
        if (newmyll<myll) {
          start <- newstart
        }
      }
    }##    start <- optim(1:4,constrLogLikS,method="SANN",control=list(maxit=50))
    optim$start <- start
  }
  
  if (is.null(optim$prob))
    optim$prob <- rep(1/k,k-1)
  thetacur <- optim$start
  probcur <- with(optim, c(prob,1-sum(prob)))
  probs <- rbind(probcur);

  thetas <- rbind(thetacur)
  if (optim$constrain) {
    thetas[constrained] <- exp(thetas[constrained])
  }
  
  gamma <- t(rmultinom(nrow(data),1,probs))
  newgamma <- gamma
##  gammas <- list()
  curloglik <- logLik(mymodel,p=thetacur,prob=probcur)
  vals <- c(curloglik)
  i <- count <- 0
  member <- rep(1,nrow(data))
  E <- dloglik <- Inf
  

  ## constrLogLik <- function(p,prob) {      
  ##   if (optim$constrain) {
  ##     p[constrained] <- exp(p[constrained])
  ##   }
  ##   logLik(mymodel,p=p,prob=prob)
  ## }
  ## EM algorithm:
  myObj <- function(p) {
    if (optim$constrain) {
      p[constrained] <- exp(p[constrained])
    }
    myp <- modelPar(mg,p)$p
    ##    save(p,file="p.rda")
    ##      lf <- sapply(1:k, function(i) logLik(mg$lvm[[i]],p=myp[[i]],data=data,indiv=TRUE))
    ##    print(lf)
    ##      ff <- exp(lf)
    ff <- sapply(1:k, function(j) logLik(mg$lvm[[j]],p=myp[[j]],data=data,indiv=TRUE))
    return(-sum(gamma*ff))
    ## Previous:
    ##      ff <- sapply(1:k, function(j) exp(logLik(mg$lvm[[j]],p=myp[[j]],data=data,indiv=TRUE)))
    ##      return(-sum(log(rowSums(gamma*ff))))
  }
  myGrad <- function(p) {
    if (optim$constrain) {
      p[constrained] <- exp(p[constrained])
    }
    myp <- modelPar(mg,p)$p
    D <- lapply(1:k, function(j) gamma[,j]*score(mg$lvm[[j]],p=myp[[j]],data=data,indiv=TRUE))
    D0 <- matrix(0,nrow(data),length(p))
    for (j in 1:k) D0[,parpos[[j]]] <- D0[,parpos[[j]]]+D[[j]]
    S <- -colSums(D0)
    if (optim$constrain) {
      S[constrained] <- S[constrained]*p[constrained]
    }
    return(S)
    ## Previous:
    ##      ff <- sapply(1:k, function(j) exp(logLik(mg$lvm[[j]],p=myp[[j]],data=data,indiv=TRUE)))
    ##      gammaff <- gamma*ff
    ##      f0 <- rowSums(gammaff)
    ##      D <- lapply(1:k, function(j) 1/f0*(gammaff)[,j]*score(mg$lvm[[j]],p=myp[[j]],data=data))
    ##      D0 <- matrix(0,nrow(data),length(p))
    ##      for (k in 1:k) D0[,parpos[[k]]] <- D0[,parpos[[k]]]+D[[k]]
    ##      -colSums(D0)
  }
  myInformation <- function(p) {
    p0 <- p
    if (optim$constrain) {
      p[constrained] <- exp(p[constrained])
    }
    myp <- modelPar(mg,p)$p    
    I <- lapply(1:k, function(j) probcur[j]*information(mg$lvm[[j]],p=myp[[j]],n=nrow(data),data=data))
    I0 <- matrix(0,length(p),length(p))
    for (j in 1:k) {
      I0[parpos[[j]],parpos[[j]]] <- I0[parpos[[j]],parpos[[j]]] + I[[j]]
    }
    if (optim$constrain) {
      I0[constrained,-constrained] <- apply(I0[constrained,-constrained,drop=FALSE],2,function(x) x*p[constrained]);
      I0[-constrained,constrained] <- t(I0[constrained,-constrained])
      D <- -myGrad(p0)
      if (length(constrained)==1)
        I0[constrained,constrained] <- I0[constrained,constrained]*outer(p[constrained],p[constrained]) + D[constrained]
      else
        I0[constrained,constrained] <- I0[constrained,constrained]*outer(p[constrained],p[constrained]) + diag(D[constrained])
    }
    return(I0)
  }
  Scoring <- function(p) {
    p.orig <- p
    if (optim$constrain) {
      p[constrained] <- exp(p[constrained])
    }
    myp <- modelPar(mg,p)$p
    D <- lapply(1:k, function(j) gamma[,j]*score(mg$lvm[[j]],p=myp[[j]],data=data,indiv=TRUE))
    D0 <- matrix(0,nrow(data),length(p))
    for (j in 1:k) D0[,parpos[[j]]] <- D0[,parpos[[j]]]+D[[j]]
    S <- colSums(D0)
    if (optim$constrain) {
      S[constrained] <- S[constrained]*p[constrained]
    }
    I <- lapply(1:k, function(j) probcur[j]*information(mg$lvm[[j]],p=myp[[j]],n=nrow(data),data=data))
    I0 <- matrix(0,length(p),length(p))
    for (j in 1:k) {
      I0[parpos[[j]],parpos[[j]]] <- I0[parpos[[j]],parpos[[j]]] + I[[j]]
    }
    if (optim$constrain) {
      I0[constrained,-constrained] <- apply(I0[constrained,-constrained,drop=FALSE],2,function(x) x*p[constrained]);
      I0[-constrained,constrained] <- t(I0[constrained,-constrained])
      if (length(constrained)==1)
        I0[constrained,constrained] <- I0[constrained,constrained]*p[constrained]^2 + S[constrained]
      else
        I0[constrained,constrained] <- I0[constrained,constrained]*outer(p[constrained],p[constrained]) + diag(S[constrained])
    }
##    print(paste(S,collapse=","))
    if (optim$stabil) {
##      I0 <- I0+S%*%t(S)
      if (optim$lambda>0)
        sigma <- optim$lambda
      else
        sigma <- (t(S)%*%S)[1]^0.5
      I0 <- I0+optim$gamma2*(sigma)*diag(nrow(I0))
    }       
    p.orig + optim$gamma*Inverse(I0)%*%S
##   p.orig + Inverse(I0+optim$lambda*diag(nrow(I0)))%*%S
  }

  on.exit(
          {
            member <- apply(gamma,1,which.max)
            res <- list(prob=probs,theta=thetas, objective=vals, gamma=newgamma, k=k, member=member, data=data, parpos=parpos, multigroup=mg, model=mg$lvm, logLik=vals);
            class(res) <- "lvm.mixture"
            res$vcov <- Inverse(information.lvm.mixture(res))
            return(res)
          }
          )


  mytheta <- thetacur
  if (optim$constrain) {
    mytheta <- thetacur
    mytheta[constrained] <- exp(mytheta[constrained])
  }

  while (i<optim$iter.max) {
##    browser()
    if (E<optim$tol) {
      if (optim$stopc<2 | abs(dloglik)<optim$ltol)
        break;
    }
    if (!missing(FUN)) {
##      if (!missing(FUN) & i>0) {
      member <- apply(gamma,1,which.max)
      res <- list(prob=probs,theta=thetas, objective=vals, gamma=newgamma, k=k, member=member, data=data, parpos=parpos, multigroup=mg, model=mg$lvm);      
      class(res) <- "lvm.mixture"
      dummy <- FUN(res)
    }
    i <- i+1

    probs <- rbind(probs,probcur)
    pp <- modelPar(mg,mytheta)$p
    logff <- sapply(1:k, function(j) (logLik(mg$lvm[[j]],p=pp[[j]],data=data,indiv=TRUE)))
##    print(probcur)
##    print(dim(logff))
##    print(logff)
##    pff <- t(apply(exp(logff),1, function(y) y*probcur))
    
    logplogff <- t(apply(logff,1, function(z) z+log(probcur)))
    ## Log-sum-exp (see e.g. NR)
    zmax <- apply(logplogff,1,max)
    logsumpff <- log(rowSums(exp(logplogff-zmax)))+zmax    

    oldloglik <- curloglik
    curloglik <- sum(logsumpff)
    dloglik <- abs(curloglik-oldloglik)
    vals <- c(vals,curloglik)
    
    count <- count+1
    if (count==optim$trace) {
      cat("Iteration ",i,"\n",sep="")
      cat("\tlogLik=",curloglik,"\n",sep="")
      cat("\tChange in logLik (1-norm)=",dloglik,"\n",sep="")
      cat("\tChange in parameter (2-norm):",E,"\n",sep="")
##      print(E>optim$tol)
      cat("\tParameter:\n")
      print(as.vector(mytheta)); count <- 0
    }


##    sumpff <- rowSums(exp(logplogff));
##    sumpff[sumpff==0] <- 1e-6
    
    gamma <- exp(apply(logplogff,2,function(y) y - logsumpff)) ## Posterior class probabilities
##    print(gamma)
    
##    gamma <- apply(logplogff,2,function(y) y - log(sumpff)) ## Posterior class probabilities
    
##    gamma <- apply(pff,2,function(y) y/sumpff) ## Posterior class probabilities
    ##  opt <- nlminb(pcur,myObj,grad=myGrad,control=list(trace=1))
    mythetaold <- mytheta
##    opt <- nlminb(thetacur,myObj,grad=myGrad, lower=lower, control=list(...))
    ##    print(thetacur)    

    
    #### M-step    
    if (type%in%c("sem","cem")) {
      if (type=="sem")
        idx <- apply(gamma,1,function(rr) which(rmultinom(1,1,rr)==1))
      else
        idx <- apply(gamma,1,function(rr) which.max(rr))

      mydata <- list()
      for (ii in 1:k) {
        mydata <- c(mydata, list(data[idx==ii,,drop=FALSE]))
      }      
      mymg <- multigroup(x,mydata,fix=FALSE)
      e <- estimate(mymg,silent=TRUE,control=list(start=mytheta))
      mytheta <- pars(e)
      probcur <- sapply(1:k,function(j) sum(idx==j)/length(idx))
      
    } else {    
      if (optim$method=="BFGS") {
        opt <- nlminb(thetacur,myObj,gradient=myGrad,hessian=myInformation,lower=lower, control=list(iter.max=10))  
        thetacur <- opt$par
      } else {     
        probcur <- colMeans(gamma)
        oldpar <- thetacur
        count2 <- 0
        for (jj in 1:optim$newton) {
          count2 <- count2+1
          ##        aa <- list(thetacur=thetacur, gamma=gamma, mg=mg, optim=optim, constrained=constrained, parpos=parpos, probcur=probcur)
          ##        save(aa, file="aa.rda")
          ##        thetacur <- thetacur + Inverse(myInformation(thetacur))%*%(-myGrad(thetacur))
          oldpar <- thetacur
          ##        print(thetacur)
          thetacur <- Scoring(thetacur)
          if (frobnorm(oldpar-thetacur)<optim$delta) break;
        }
        cat(count2, " Scoring iterations.\n")
      }    
      if (optim$constrain) {
        mytheta <- thetacur
        mytheta[constrained] <- exp(mytheta[constrained])
      }
    }
      thetas <- rbind(thetas,as.vector(mytheta))
    newgamma <- gamma
##      gammas <- c(gammas, list(gamma))
      E <- sum((mytheta-mythetaold)^2)
  }
}

###}}} mixture


model.frame.lvm.mixture <- function(formula,...) {
    return(formula$data)
}

iid.lvm.mixture <- function(x,...) {
    bread <- vcov(x)
    structure(t(bread%*%t(score(x,indiv=TRUE))),bread=bread)
}

manifest.lvm.mixture <- function(x,...) {
    manifest(x$multigroup,...)
}

predict.lvm.mixture <- function(object,x=NULL,p=coef(object,full=TRUE),...) {
    p0 <- coef(object)
    pp <- p[seq_along(p0)]
    pr <- p[length(p0)+seq(length(p)-length(p0))];
    if (length(pr)<object$k) pr <- c(pr,1-sum(pr))
    myp <- modelPar(object$multigroup,p=pp)$p
    
    logff <- sapply(seq(object$k), function(j) (logLik(object$multigroup$lvm[[j]],p=myp[[j]],data=object$data,indiv=TRUE)))
    logplogff <- t(apply(logff,1, function(y) y+log(pr)))
    zmax <- apply(logplogff,1,max)
    logsumpff <- log(rowSums(exp(logplogff-zmax)))+zmax
    aji <- apply(logplogff,2,function(x) exp(x-logsumpff))
    gamma <- exp(apply(logplogff,2,function(y) y - logsumpff)) ## Posterior class probabilities    
    M <- 0; V <- 0
    for (i in seq(object$k)) {
        m <- Model(object$multigroup)[[i]]
        P <- predict(m,data=object$data,p=myp[[i]],x=x)
        M <- M+gamma[,i]*P
        V <- V+gamma[,i]^2*attributes(P)$cond.var
    }
    structure(M,cond.var=V)
}

###{{{ logLik

ll  <- function(object,p=coef(object),prob) {
  myp <- modelPar(object$multigroup,p)$p
  ff <- sapply(1:object$k, function(j) exp(logLik(object$multigroup$lvm[[j]],p=myp[[j]],data=object$data,indiv=TRUE)))
  if (missing(prob))
    prob <- coef(object,prob=TRUE)
  ##  gamma <- tail(object$gamma,1)[[1]]
##  sum(log(colSums((prob*t(ff*gamma)))))
  ##  sum(log(colSums((prob*t(ff)))))
  loglik <- sum(log(colSums((prob*t(ff)))))
  npar <- length(prob)-1 + length(p)
  nobs <- nrow(object$data)
  attr(loglik, "nall") <- nobs
  attr(loglik, "nobs") <- nobs-npar
  attr(loglik, "df") <- npar
  class(loglik) <- "logLik"  
  return(loglik)
}

score.lvm.mixture <- function(x,theta=c(p,prob),p=coef(x),prob,indiv=FALSE,...) {
  ##  browser()
  myp <- modelPar(x$multigroup,p)$p
  if (missing(prob))
    prob <- coef(x,prob=TRUE)
  if (length(prob)<x$k)
    prob <- c(prob,1-sum(prob))
  logff <- sapply(seq(x$k), function(j) (logLik(x$multigroup$lvm[[j]],p=myp[[j]],data=x$data,indiv=TRUE)))
  logplogff <- t(apply(logff,1, function(y) y+log(prob)))
  zmax <- apply(logplogff,1,max)
  logsumpff <- log(rowSums(exp(logplogff-zmax)))+zmax
  aji <- apply(logplogff,2,function(x) exp(x-logsumpff))
  
  scoref <- lapply(score(x$multigroup,p=p,indiv=TRUE),                   
                   function(x) { x[which(is.na(x))] <- 0; x })

  Stheta <- matrix(0,ncol=ncol(scoref[[1]]),nrow=nrow(scoref[[1]]))
  Spi <- matrix(0,ncol=x$k-1,nrow=nrow(Stheta))
  for (j in 1:x$k) {
    Stheta <- Stheta + apply(scoref[[j]],2,function(x) x*aji[,j])
    if (j<x$k)
      Spi[,j] <- aji[,j]/prob[j] - aji[,x$k]/prob[x$k]
  }
  S <- cbind(Stheta,Spi)
  if (!indiv)
    return(colSums(S))
  return(S)
}

information.lvm.mixture <- function(x,...) {
  S <- score.lvm.mixture(x,indiv=TRUE,...)
  res <- t(S)%*%S
  attributes(res)$grad <- colSums(S)
  return(res)
}

logLik.lvm.mixture <- function(object,theta=c(p,prob),p=coef(object),prob,...) {
  myp <- modelPar(object$multigroup,p)$p
  if (missing(prob))
    prob <- coef(object,prob=TRUE)
  if (length(prob)<object$k)
    prob <- c(prob,1-sum(prob))
  logff <- sapply(1:object$k, function(j) (logLik(object$multigroup$lvm[[j]],p=myp[[j]],data=object$data,indiv=TRUE)))
  logplogff <- t(apply(logff,1, function(y) y+log(prob)))
  ## Log-sum-exp (see e.g. NR)
  zmax <- apply(logplogff,1,max)
  logsumpff <- log(rowSums(exp(logplogff-zmax)))+zmax    
  loglik <- sum(logsumpff)
  npar <- length(prob)-1 + length(p)
  nobs <- nrow(object$data)
  attr(loglik, "nall") <- nobs
  attr(loglik, "nobs") <- nobs-npar
  attr(loglik, "df") <- npar
  class(loglik) <- "logLik"  
  return(loglik)
}

###}}} logLik

###{{{ vcov

vcov.lvm.mixture <- function(object,...) {
  return(object$vcov)
}

###}}}z

###{{{ summary/print

summary.lvm.mixture <- function(object,labels=0,...) {
  mm <- object$multigroup$lvm
  p <- coef(object,list=TRUE)
  p0 <- coef(object)
  myp <- modelPar(object$multigroup,1:length(p0))$p
  coefs <- list()
  ncluster <- c()
  for (i in 1:length(mm)) {
    ## cc <- coef(mm[[i]],p=p[[i]],vcov=vcov(object)[myp[[i]],myp[[i]]],data=NULL)
    ## nn <- coef(mm[[i]],mean=TRUE,labels=labels,symbol="<-")
    ## nm <- index(mm[[i]])$npar.mean
    ## if (nm>0) {
    ##   nn <- c(nn[-(1:nm)],nn[1:nm])
    ## }
    ## rownames(cc) <- nn
    ## attributes(cc)[c("type","var","from","latent")] <- NULL
    cc <- CoefMat(mm[[i]],p=p[[i]],vcov=vcov(object)[myp[[i]],myp[[i]]],data=NULL,labels=labels)
    coefs <- c(coefs, list(cc))
    ncluster <- c(ncluster,sum(object$member==i))
  }
  res <- list(coef=coefs,ncluster=ncluster,prob=tail(object$prob,1),
              AIC=AIC(object),s2=sum(score(object)^2))
  class(res) <- "summary.lvm.mixture"
  return(res)
}

print.summary.lvm.mixture <- function(x,...) {
  space <- paste(rep(" ",12),collapse="")
  for (i in 1:length(x$coef)) {
    cat("Cluster ",i," (n=",x$ncluster[i],", Prior=", formatC(x$prob[i]),"):\n",sep="")
    cat(rep("-",50),"\n",sep="")
    print(x$coef[[i]], quote=FALSE)
    if (i<length(x$coef)) cat("\n")
  }
  cat(rep("-",50),"\n",sep="")
  cat("AIC=",x$AIC,"\n")
  cat("||score||^2=",x$s2,"\n")
  invisible(par)  
}

print.lvm.mixture <- function(x,...) {
  space <- paste(rep(" ",12),collapse="")
  for (i in 1:x$k) {
    cat("Cluster ",i," (n=",sum(x$member==i),"):\n",sep="")
    cat(rep("-",50),"\n",sep="")
    print(coef(x)[x$parpos[[i]]], quote=FALSE)
    cat("\n")   
  }
  invisible(par)
}

###}}}

###{{{ plot

plot.lvm.mixture <- function(x,type="l",...) {
  matplot(x$theta,type=type,...)
}

###}}} plot

###{{{ coef

coef.lvm.mixture <- function(object,iter,list=FALSE,full=FALSE,prob=FALSE,class=FALSE,...) {
  N <- nrow(object$theta)
  res <- object$theta
  if (class) return(object$gammas)
  if (list) {
      res <- list()
      for (i in 1:object$k) 
          res <- c(res, list(coef(object)[object$parpos[[i]]]))
      return(res)
  }
  if (full) {
      res <- cbind(res,object$prob[,seq(ncol(object$prob)-1)])
  }   
  if (prob) {
      res <- object$prob
  }
  if (missing(iter))
      return(res[N,])
  else
      return(res[iter,])
}

###}}} coef

Try the lava.mixture package in your browser

Any scripts or data that you put into this service are public.

lava.mixture documentation built on May 31, 2017, 4:35 a.m.