R/intensity.fd.R

In fda: Functional Data Analysis

intensity.fd <- function(x, WfdParobj, conv=0.0001, iterlim=20, dbglev=1, 
                            returnMatrix=FALSE) {
# INTENSITYFD estimates the intensity function \lambda(x) of a
#  nonhomogeneous Poisson process from a sample of event times.

#  Arguments are:
#  X         ... data value array.
#  WFDPAROBJ ... functional parameter object specifying the initial log
#              density, the linear differential operator used to smooth
#              smooth it, and the smoothing parameter.
#  CONV      ... convergence criterion
#  ITERLIM   ... iteration limit for scoring iterations
#  DBGLEV    ... level of output of computation history

#  Returns:
#  A list containing
#  WFDOBJ ...   functional data basis object defining final log intensity
#  FLIST  ...   Struct object containing
#               FSTR$f     final log likelihood
#               FSTR$norm  final norm of gradient
#  ITERNUM   Number of iterations
#  ITERHIST  History of iterations
#  RETURNMATRIX ... If False, a matrix in sparse storage model can be returned
#               from a call to function BsplineS.  See this function for
#               enabling this option.

#  last modified 10 May 2012 by Jim Ramsay

	#  check WfdParobj
	
	if (!inherits(WfdParobj, "fdPar"))
		if (inherits(WfdParobj, "fd") || inherits(WfdParobj, "basisfd"))
			WfdParobj <- fdPar(WfdParobj)
		else stop("WFDPAROBJ is not a fdPar object")
					
	#  set up WFDOBJ

	Wfdobj   <- WfdParobj$fd

	#  set up LFDOBJ
	
	Lfdobj <- WfdParobj$Lfd
	Lfdobj <- int2Lfd(Lfdobj)

	#  set up BASIS

	basisobj <- Wfdobj$basis
	nbasis   <- basisobj$nbasis
	rangex   <- basisobj$rangeval
	active   <- 1:nbasis
	
	x    <- as.vector(x)
	N    <- length(x)

	#  check for values outside of the range of WFD0

	inrng <- (1:N)[x >= rangex[1] & x <= rangex[2]]
	if (length(inrng) != N) {
		print(c(length(inrng), N))
		print(c(rangex[1], rangex[2], min(x), max(x)))
    	warning("Some values in X out of range and not used.")
	}

	x     <- x[inrng]
	nobs  <- length(x)

	#  set up some arrays

	climit    <- c(rep(-50,nbasis),rep(400,nbasis))
	cvec0     <- Wfdobj$coefs
	hmat      <- matrix(0,nbasis,nbasis)
	dbgwrd    <- dbglev > 1

	#  initialize matrix Kmat defining penalty term

	lambda <- WfdParobj$lambda
	if (lambda > 0) Kmat <- lambda*getbasispenalty(basisobj, Lfdobj)
  
	#  evaluate log likelihood
	#    and its derivatives with respect to these coefficients

	result <- loglfninten(x, basisobj, cvec0, returnMatrix)
	logl   <- result[[1]]
	Dlogl  <- result[[2]]

	#  compute initial badness of fit measures

	f0    <- -logl
	gvec0 <- -Dlogl
	if (lambda > 0) {
   		gvec0 <- gvec0 +           2*(Kmat %*% cvec0)
   		f0    <- f0    + t(cvec0) %*% Kmat %*% cvec0
	}
	Foldstr <- list(f = f0, norm = sqrt(mean(gvec0^2)))

	#  compute the initial expected Hessian

	hmat0 <- Varfninten(basisobj, cvec0, returnMatrix)
	if (lambda > 0) hmat0 <- hmat0 + 2*Kmat

	#  evaluate the initial update vector for correcting the initial bmat

	deltac   <- -solve(hmat0,gvec0)
	cosangle <- -sum(gvec0*deltac)/sqrt(sum(gvec0^2)*sum(deltac^2))

	#  initialize iteration status arrays

	iternum <- 0
	status <- c(iternum, Foldstr$f, -logl, Foldstr$norm)
	if (dbglev > 0) {
		cat("Iteration  Criterion  Neg. Log L  Grad. Norm\n")
		cat("      ")
		cat(format(iternum))
		cat("    ")
		cat(format(status[2:4]))
		cat("\n")
	}
	iterhist <- matrix(0,iterlim+1,length(status))
	iterhist[1,]  <- status
	
	#  quit if ITERLIM == 0
	
	if (iterlim == 0) {
    	Flist     <- Foldstr
    	iterhist <- iterhist[1,]
    	return( list(Wfdobj=Wfdobj, Flist=Flist, iternum=iternum, iterhist=iterhist) )
	} else {
		gvec <- gvec0
		hmat <- hmat0
	}

	#  -------  Begin iterations  -----------

	STEPMAX <- 5
	MAXSTEP <- 400
	trial   <- 1
	cvec    <- cvec0
	linemat <- matrix(0,3,5)

	for (iter in 1:iterlim) {
   		iternum <- iternum + 1
	   	#  take optimal stepsize
   		dblwrd <- c(0,0)
		  limwrd <- c(0,0)
		  stpwrd <- 0
		  ind    <- 0
	   	#  compute slope
      	Flist <- Foldstr
      	linemat[2,1] <- sum(deltac*gvec)
      	#  normalize search direction vector
      	sdg     <- sqrt(sum(deltac^2))
      	deltac  <- deltac/sdg
      	dgsum   <- sum(deltac)
      	linemat[2,1] <- linemat[2,1]/sdg
      	#  return with stop condition if (initial slope is nonnegative
      	if (linemat[2,1] >= 0) {
        	print("Initial slope nonnegative.")
        	ind <- 3
        	iterhist <- iterhist[1:(iternum+1),]
        	break
      	}
      	#  return successfully if (initial slope is very small
      	if (linemat[2,1] >= -1e-5) {
        	if (dbglev>1) print("Initial slope too small")
        	iterhist <- iterhist[1:(iternum+1),]
        	break
      	}
    	#  load up initial search matrix
      	linemat[1,1:4] <- 0
      	linemat[2,1:4] <- linemat[2,1]
      	linemat[3,1:4] <- Foldstr$f
     	#  output initial results for stepsize 0
     	stepiter  <- 0
      	if (dbglev > 1) {
			cat("              ")
			cat(format(stepiter))
			cat(format(linemat[,1]))
			cat("\n")
		}
      	ips <- 0
      	#  first step set to trial
      	linemat[1,5]  <- trial
      	#  Main iteration loop for linesrch
      	for (stepiter in 1:STEPMAX) {
        	#  ensure that step does not go beyond limits on parameters
        	limflg  <- 0
        	#  check the step size
        	result <- stepchk(linemat[1,5], cvec, deltac, limwrd, ind,
                            climit, active, dbgwrd)
			linemat[1,5] <- result[[1]]
			ind          <- result[[2]]
			limwrd       <- result[[3]]
       	if (linemat[1,5] <= 1e-9) {
          		#  Current step size too small  terminate
          		Flist   <- Foldstr
          		cvecnew <- cvec
          		gvecnew <- gvec
          		if (dbglev > 1) print(paste("Stepsize too small:", linemat[1,5]))
          		if (limflg) ind <- 1 else ind <- 4
          		break
        	}
        	cvecnew <- cvec + linemat[1,5]*deltac
        	#  compute new function value and gradient
			    result  <- loglfninten(x, basisobj, cvecnew, returnMatrix)
			    logl    <- result[[1]]
			    Dlogl   <- result[[2]]
        	Flist$f <- -logl
        	gvecnew <- -Dlogl
        	if (lambda > 0) {
            	gvecnew <- gvecnew + 2*Kmat %*% cvecnew
            	Flist$f <- Flist$f + t(cvecnew) %*% Kmat %*% cvecnew
        	}
        	Flist$norm <- sqrt(mean(gvecnew^2))
        	linemat[3,5] <- Flist$f
        	#  compute new directional derivative
        	linemat[2,5] <- sum(deltac*gvecnew)
      		if (dbglev > 1) {
				cat("              ")
				cat(format(stepiter))
				cat(format(linemat[,1]))
				cat("\n")
			}
        	#  compute next step
			result  <- stepit(linemat, ips, dblwrd, MAXSTEP)
			linemat <- result[[1]]
			ips     <- result[[2]]
			ind     <- result[[3]]
			dblwrd  <- result[[4]]
        	trial   <- linemat[1,5]
        	#  ind == 0 implies convergence
        	if (ind == 0 | ind == 5) break
        	#  end of line search loop
     	}

    	#  update current parameter vectors

    	cvec <- cvecnew
     	gvec <- gvecnew
	  	Wfdobj$coefs <- cvec
     	status <- c(iternum, Flist$f, -logl, Flist$norm)
     	iterhist[iter+1,] <- status
		cat("      ")
		cat(format(iternum))
		cat("    ")
		cat(format(status[2:4]))
		cat("\n")

     	#  test for convergence

     	if (abs(Flist$f-Foldstr$f) < conv) {
       	iterhist <- iterhist[1:(iternum+1),]
			denslist <- list("Wfdobj" = Wfdobj, "Flist" = Flist,
			          			"iternum" = iternum, "iterhist" = iterhist)
			return( denslist )
     	}
     	if (Flist$f >= Foldstr$f) break
     	#  compute the Hessian
     	hmat <- Varfninten(basisobj, cvec, returnMatrix)
     	if (lambda > 0) hmat <- hmat + 2*Kmat
     	#  evaluate the update vector
     	deltac <- -solve(hmat,gvec)
     	cosangle  <- -sum(gvec*deltac)/sqrt(sum(gvec^2)*sum(deltac^2))
     	if (cosangle < 0) {
       	if (dbglev > 1) print("cos(angle) negative")
       	deltac <- -gvec
     	}
     	Foldstr <- Flist
		#  end of iterations
  	}
	#  return final results
	intenslist <- list("Wfdobj" = Wfdobj, "Flist" = Flist,
			          "iternum" = iternum, "iterhist" = iterhist)
 	return( intenslist )
}

#  ---------------------------------------------------------------

loglfninten <- function(x, basisobj, cvec, returnMatrix=FALSE) {
	#  Computes the log likelihood and its derivative with
	#    respect to the coefficients in CVEC
   	nobs    <- length(x)
   	cval    <- normint.phi(basisobj, cvec, returnMatrix=returnMatrix)
   	phimat  <- getbasismatrix(x, basisobj, 0, returnMatrix)
   	logl    <- sum(phimat %*% cvec) - cval
	  EDW     <- expect.phi(basisobj, cvec, returnMatrix=returnMatrix)
   	Dlogl   <- apply(phimat,2,sum) - EDW
	return( list(logl, Dlogl) )
}

#  ---------------------------------------------------------------

Varfninten <- function(basisobj, cvec, returnMatrix=FALSE) {
	#  Computes the expected Hessian
   	Varphi  <- expect.phiphit(basisobj, cvec, returnMatrix=returnMatrix)
	return(Varphi)
}
	
#  ---------------------------------------------------------------

normint.phi <- function(basisobj, cvec, JMAX=15, EPS=1e-7, returnMatrix=FALSE) 
{

#  Computes integrals of
#      p(x) = exp phi'(x) %*% cvec
#  by numerical integration using Romberg integration

  	#  check arguments, and convert basis objects to functional data objects

  	if (!inherits(basisobj, "basisfd") )
    	stop("First argument must be a basis function object.")

	  nbasis <- basisobj$nbasis
	  rng    <- basisobj$rangeval
  	oneb   <- matrix(1,1,nbasis)

  	#  set up first iteration

  	width <- rng[2] - rng[1]
  	JMAXP <- JMAX + 1
  	h <- matrix(1,JMAXP,1)
  	h[2] <- 0.25
  	#  matrix SMAT contains the history of discrete approximations to the integral
  	smat <- matrix(0,JMAXP,1)
  	#  the first iteration uses just the }points
  	x  <- rng
  	nx <- length(x)
  	ox <- matrix(1,nx,1)
  	fx <- getbasismatrix(x, basisobj, 0, returnMatrix)
  	wx <- fx %*% cvec
  	wx[wx < -50] <- -50
  	px <- exp(wx)
  	smat[1]  <- width*sum(px)/2
  	tnm <- 0.5
  	j   <- 1

  	#  now iterate to convergence
  	for (j in 2:JMAX) {
    	tnm  <- tnm*2
    	del  <- width/tnm
    	if (j == 2) {
      		x <- (rng[1] + rng[2])/2
    	} else {
      		x <- seq(rng[1]+del/2, rng[2], del)
    	}
    	fx <- getbasismatrix(x, basisobj, 0, returnMatrix)
    	wx <- fx %*% cvec
    	wx[wx < -50] <- -50
    	px <- exp(wx)
    	smat[j] <- (smat[j-1] + width*sum(px)/tnm)/2
    	if (j >= 5) {
      		ind <- (j-4):j
			result <- polintarray(h[ind],smat[ind],0)
			ss  <- result[[1]]
			dss <- result[[2]]
      		if (!any(abs(dss) >= EPS*max(abs(ss)))) {
        		#  successful convergence
        		return(ss)
      		}
    	}
    	smat[j+1] <- smat[j]
    	h[j+1]    <- 0.25*h[j]
 	}
  	warning(paste("No convergence after ",JMAX," steps in NORMALIZE.PHI"))
	return(ss)
}

#  ---------------------------------------------------------------

expect.phi <- function(basisobj, cvec, nderiv=0, JMAX=15, EPS=1e-7, 
                       returnMatrix=FALSE) {
#  Computes expectations of basis functions with respect to intensity
#      p(x) <- exp t(c)*phi(x)
#  by numerical integration using Romberg integration

  	#  check arguments, and convert basis objects to functional data objects

  	if (!inherits(basisobj, "basisfd"))
    	stop("First argument must be a basis function object.")

  	nbasis <- basisobj$nbasis
  	rng    <- basisobj$rangeval
  	oneb   <- matrix(1,1,nbasis)

  	#  set up first iteration

  	width <- rng[2] - rng[1]
  	JMAXP <- JMAX + 1
  	h <- matrix(1,JMAXP,1)
  	h[2] <- 0.25
  	#  matrix SMAT contains the history of discrete approximations to the integral
  	smat <- matrix(0,JMAXP,nbasis)
  	sumj <- matrix(0,1,nbasis)
  	#  the first iteration uses just the }points
  	x  <- rng
  	nx <- length(x)
  	ox <- matrix(1,nx,nx)
  	fx <- as.matrix(getbasismatrix(x, basisobj, 0, returnMatrix))
  	wx <- fx %*% cvec
  	wx[wx < -50] <- -50
  	px <- exp(wx)
  	if (nderiv == 0) {
    	  Dfx <- fx
  	} else {
    	  Dfx <- as.matrix(getbasismatrix(x, basisobj, 1, returnMatrix))
  	}
  	sumj <- t(Dfx) %*% px
  	smat[1,]  <- width*sumj/2
  	tnm <- 0.5
  	j   <- 1

  	#  now iterate to convergence

  	for (j in 2:JMAX) {
    	tnm  <- tnm*2
    	del  <- width/tnm
    	if (j == 2) {
        x <- (rng[1] + rng[2])/2
    	} else {
        x <- seq(rng[1]+del/2, rng[2], del)
    	}
    	nx <- length(x)
    	fx <- as.matrix(getbasismatrix(x, basisobj, 0, returnMatrix))
    	wx <- fx %*% cvec
    	wx[wx < -50] <- -50
    	px <- exp(wx)
    	if (nderiv == 0) {
        Dfx <- fx
    	} else {
        Dfx <- as.matrix(getbasismatrix(x, basisobj, 1, returnMatrix))
    	}
    	sumj <- t(Dfx) %*% px
    	smat[j,] <- (smat[j-1,] + width*sumj/tnm)/2
    	if (j >= 5) {
      		ind <- (j-4):j
      		temp <- smat[ind,]
			result <- polintarray(h[ind],temp,0)
			ss  <- result[[1]]
			dss <- result[[2]]
      		if (!any(abs(dss) > EPS*max(abs(ss)))) {
        		#  successful convergence
        		return(ss)
      		}
    	}
    	smat[j+1,] <- smat[j,]
    	h[j+1] <- 0.25*h[j]
  	}
  	warning(paste("No convergence after ",JMAX," steps in EXPECT.PHI"))
	return(ss)
}

#  ---------------------------------------------------------------

expect.phiphit <- function(basisobj, cvec, nderiv1=0, nderiv2=0,
                           JMAX=15, EPS=1e-7, returnMatrix=FALSE) {

#  Computes expectations of cross product of basis functions with
#  respect to intensity
#      p(x) = exp t(c) %*% phi(x)
#  by numerical integration using Romberg integration

  	#  check arguments, and convert basis objects to functional data objects

  	if (!inherits(basisobj, "basisfd"))
    	stop("First argument must be a basis function object.")

  	nbasis <- basisobj$nbasis
  	rng    <- basisobj$rangeval
  	oneb   <- matrix(1,1,nbasis)

  	#  set up first iteration

  	width <- rng[2] - rng[1]
  	JMAXP <- JMAX + 1
  	h <- matrix(1,JMAXP,1)
  	h[2] <- 0.25
  	#  matrix SMAT contains the history of discrete approximations to the integral
  	smat <- array(0,c(JMAXP,nbasis,nbasis))
  	#  the first iteration uses just the }points
  	x  <- rng
  	nx <- length(x)
  	fx <- as.matrix(getbasismatrix(x, basisobj, 0, returnMatrix))
  	wx <- fx %*% cvec
  	wx[wx < -50] <- -50
  	px <- exp(wx)
  	if (nderiv1 == 0) {
    	  Dfx1 <- fx
  	} else {
    	  Dfx1 <- as.matrix(getbasismatrix(x, basisobj, 1, returnMatrix))
  	}
  	if (nderiv2 == 0) {
    	  Dfx2 <- fx
  	} else {
    	  Dfx2 <- as.matrix(getbasismatrix(x, basisobj, 1, returnMatrix))
  	}
  	oneb <- matrix(1,1,nbasis)
  	sumj <- t(Dfx1) %*% ((px %*% oneb) * Dfx2)
  	smat[1,,]  <- width*sumj/2
  	tnm <- 0.5
  	j   <- 1

  	#  now iterate to convergence
  	for (j in 2:JMAX) {
    	tnm  <- tnm*2
    	del  <- width/tnm
    	if (j == 2) {
        x <- (rng[1] + rng[2])/2
    	} else {
        x <- seq(rng[1]+del/2, rng[2], del)
    	}
    	nx <- length(x)
    	fx <- as.matrix(getbasismatrix(x, basisobj, 0, returnMatrix))
    	wx <- fx %*% cvec
    	wx[wx < -50] <- -50
    	px <- exp(wx)
    	if (nderiv1 == 0) {
        Dfx1 <- fx
    	} else {
        Dfx1 <- as.matrix(getbasismatrix(x, basisobj, 1, returnMatrix))
    	}
    	if (nderiv2 == 0) {
        Dfx2 <- fx
    	} else {
        Dfx2 <- as.matrix(getbasismatrix(x, basisobj, 2, returnMatrix))
    	}
    	sumj <- t(Dfx1) %*% ((px %*% oneb) * Dfx2)
    	smat[j,,] <- (smat[j-1,,] + width*sumj/tnm)/2
    	if (j >= 5) {
      		ind <- (j-4):j
      		temp <- smat[ind,,]
	   		result <- polintarray(h[ind],temp,0)
	   		ss  <- result[[1]]
	   		dss <- result[[2]]
      		if (!any(abs(dss) > EPS*max(max(abs(ss))))) {
        		#  successful convergence
        		return(ss)
      		}
    	}
    	smat[j+1,,] <- smat[j,,]
    	h[j+1] <- 0.25*h[j]
  	}
  	warning(paste("No convergence after ",JMAX," steps in EXPECT.PHIPHIT"))
	return(ss)
}
#  ---------------------------------------------------------------

polintarray <- function(xa, ya, x0) {
  	#  YA is an array with up to 4 dimensions
  	#     with 1st dim the same length same as the vector XA
  	n     <- length(xa)
  	yadim <- dim(ya)
  	if (is.null(yadim)) {
		yadim <- n
		nydim <- 1
  	} else {
    	nydim <- length(yadim)
  	}
  	if (yadim[1] != n) stop("First dimension of YA must match XA")
  	difx <- xa - x0
  	absxmxa <- abs(difx)
  	ns <- min((1:n)[absxmxa == min(absxmxa)])
  	cs <- ya
  	ds <- ya
  	if (nydim == 1) y <- ya[ns]
  	if (nydim == 2) y <- ya[ns,]
  	if (nydim == 3) y <- ya[ns,,]
  	if (nydim == 4) y <- ya[ns,,,]
  	ns <- ns - 1
  	for (m in 1:(n-1)) {
    	if (nydim == 1) {
      		for (i in 1:(n-m)) {
        		ho    <- difx[i]
        		hp    <- difx[i+m]
        		w     <- (cs[i+1] - ds[i])/(ho - hp)
        		ds[i] <- hp*w
        		cs[i] <- ho*w
      		}
      		if (2*ns < n-m) {
        		dy <- cs[ns+1]
      		} else {
        		dy <- ds[ns]
        		ns <- ns - 1
      		}
  		}
  		if (nydim == 2) {
      		for (i in 1:(n-m)) {
        		ho     <- difx[i]
        		hp     <- difx[i+m]
        		w      <- (cs[i+1,] - ds[i,])/(ho - hp)
        		ds[i,] <- hp*w
        		cs[i,] <- ho*w
      		}
      		if (2*ns < n-m) {
        		dy <- cs[ns+1,]
      		} else {
        		dy <- ds[ns,]
        		ns <- ns - 1
      		}
  		}
   		if (nydim == 3) {
      		for (i in 1:(n-m)) {
        		ho       <- difx[i]
        		hp       <- difx[i+m]
        		w        <- (cs[i+1,,] - ds[i,,])/(ho - hp)
        		ds[i,,] <- hp*w
        		cs[i,,] <- ho*w
      		}
      		if (2*ns < n-m) {
        		dy <- cs[ns+1,,]
      		} else {
        		dy <- ds[ns,,]
        		ns <- ns - 1
      		}
  		}
   		if (nydim == 4) {
      		for (i in 1:(n-m)) {
        		ho      <- difx[i]
        		hp      <- difx[i+m]
        		w       <- (cs[i+1,,,] - ds[i,,,])/(ho - hp)
        		ds[i,,,] <- hp*w
        		cs[i,,,] <- ho*w
      		}
      		if (2*ns < n-m) {
        		dy <- cs[ns+1,,,]

      		} else {
        		dy <- ds[ns,,,]
        		ns <- ns - 1
      		}
  		}
   		y <- y + dy
	}
   	return( list(y, dy) )
}