1. AneuFinder
  2. Commit ecb41d4aa7858657702ab4e3e65d6757674403dd
Browse code

first working version

chakalakka authored on 06/05/2014 18:46:07
Showing 25 changed files
NAMESPACE
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@@ -5,7 +5,9 @@ useDynLib(aneufinder)
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 export(
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 bed2bin,
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 bam2bin,
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-bedGraph2bin
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+bedGraph2bin,
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+
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+find.aneuploidies
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 )
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 # Import all packages listed as Imports or Depends
R/find.aneuploidies.R
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new file mode 100644
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+find.aneuploidies <- function(binned.data, eps=0.001, max.time=-1, max.it=-1, num.trials=1, eps.try=NULL, num.threads=1, output.if.not.converged=FALSE, filter.reads=TRUE) {
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+
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+	## Intercept user input
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+	if (check.positive(eps)!=0) stop("argument 'eps' expects a positive numeric")
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+	if (check.integer(max.time)!=0) stop("argument 'max.time' expects an integer")
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+	if (check.integer(max.it)!=0) stop("argument 'max.it' expects an integer")
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+	if (check.positive.integer(num.trials)!=0) stop("argument 'num.trials' expects a positive integer")
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+	if (!is.null(eps.try)) {
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+		if (check.positive(eps.try)!=0) stop("argument 'eps.try' expects a positive numeric")
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+	}
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+	if (check.positive.integer(num.threads)!=0) stop("argument 'num.threads' expects a positive integer")
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+	if (check.logical(output.if.not.converged)!=0) stop("argument 'output.if.not.converged' expects a logical (TRUE or FALSE)")
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+
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+
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+	war <- NULL
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+	if (is.null(eps.try)) eps.try <- eps
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+
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+	coordinatenames <- c("chrom","start","end","reads")
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+	names(binned.data) <- coordinatenames
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+
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+	## Assign variables
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+	statelabels <- c("unmappable","monosomy","disomy","trisomy","tetrasomy")
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+	numstates <- length(statelabels)
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+	numbins <- length(binned.data$reads)
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+
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+	# Check if there are reads in the data, otherwise HMM will blow up
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+	if (!any(binned.data$reads!=0)) {
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+		stop("All reads in data are zero. No univariate HMM done.")
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+	}
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+
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+	# Filter high reads out, makes HMM faster
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+	if (filter.reads) {
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+		limit <- 10*ceiling(var(binned.data$reads))
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+		mask <- binned.data$reads > limit
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+		binned.data$reads[mask] <- limit
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+		numfiltered <- length(which(mask))
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+		if (numfiltered > 0) {
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+			warning(paste("There are very high read counts (probably artificial) in your data. Replaced read counts > ",limit," (10*variance) by ",limit," in ",numfiltered," bins. Set option 'filter.reads=FALSE' to disable this filtering.", sep=""))
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+		}
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+	}
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+
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+
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+	## Call univariate in a for loop to enable multiple trials
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+	modellist <- list()
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+	for (i_try in 1:num.trials) {
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+		cat("\n\nTry ",i_try," of ",num.trials," ------------------------------\n")
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+		hmm <- .C("R_univariate_hmm",
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+			reads = as.integer(binned.data$reads), # double* O
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+			num.bins = as.integer(numbins), # int* T
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+			num.states = as.integer(numstates), # int* N
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+			means = double(length=numstates), # double* means
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+			variances = double(length=numstates), # double* variances
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+			num.iterations = as.integer(max.it), #  int* maxiter
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+			time.sec = as.integer(max.time), # double* maxtime
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+			loglik.delta = as.double(eps.try), # double* eps
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+			posteriors = double(length=numbins * numstates), # double* posteriors
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+			A = double(length=numstates*numstates), # double* A
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+			proba = double(length=numstates), # double* proba
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+			loglik = double(length=1), # double* loglik
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+			weights = double(length=numstates), # double* weights
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+			means.initial = double(length=numstates), # double* initial_means
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+			variances.initial = double(length=numstates), # double* initial_variances
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+			A.initial = double(length=numstates*numstates), # double* initial_A
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+			proba.initial = double(length=numstates), # double* initial_proba
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+			use.initial.params = as.logical(0), # bool* use_initial_params
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+			num.threads = as.integer(num.threads) # int* num_threads
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+		)
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+
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+		hmm$eps <- eps.try
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+		hmm$A <- matrix(hmm$A, ncol=hmm$num.states, byrow=TRUE)
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+		rownames(hmm$A) <- statelabels
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+		colnames(hmm$A) <- statelabels
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+		hmm$distributions <- cbind(mean=hmm$means, variance=hmm$variances, size=fsize(hmm$means,hmm$variances), prob=fprob(hmm$means,hmm$variances))
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+		rownames(hmm$distributions) <- statelabels
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+		hmm$A.initial <- matrix(hmm$A.initial, ncol=hmm$num.states, byrow=TRUE)
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+		rownames(hmm$A.initial) <- statelabels
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+		colnames(hmm$A.initial) <- statelabels
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+		hmm$distributions.initial <- cbind(mean=hmm$means.initial, variance=hmm$variances.initial, size=fsize(hmm$means.initial,hmm$variances.initial), prob=fprob(hmm$means.initial,hmm$variances.initial))
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+		rownames(hmm$distributions.initial) <- statelabels
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+		if (num.trials > 1) {
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+			if (hmm$loglik.delta > hmm$eps) {
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+				warning("HMM did not converge in trial run ",i_try,"!\n")
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+			}
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+			# Store model in list
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+			hmm$posteriors <- NULL
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+			hmm$reads <- NULL
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+			modellist[[i_try]] <- hmm
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+		}
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+	}
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+
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+	if (num.trials > 1) {
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+
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+		# Select fit with best loglikelihood
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+		indexmax <- which.max(unlist(lapply(modellist,"[[","loglik")))
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+		hmm <- modellist[[indexmax]]
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+
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+		# Rerun the HMM with different epsilon and initial parameters from trial run
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+		cat("\n\nRerunning try ",indexmax," with eps =",eps,"--------------------\n")
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+		hmm <- .C("R_univariate_hmm",
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+			reads <- as.integer(binned.data$reads), # double* O
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+			num.bins <- as.integer(numbins), # int* T
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+			num.states <- as.integer(numstates), # int* N
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+			means <- double(length=numstates), # double* means
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+			variances <- double(length=numstates), # double* variances
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+			num.iterations <- as.integer(max.it), #  int* maxiter
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+			time.sec <- as.integer(max.time), # double* maxtime
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+			loglik.delta <- as.double(eps), # double* eps
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+			posteriors <- double(length=numbins * numstates), # double* posteriors
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+			A <- double(length=numstates*numstates), # double* A
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+			proba <- double(length=numstates), # double* proba
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+			loglik <- double(length=1), # double* loglik
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+			weights <- double(length=numstates), # double* weights
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+			means.initial <- as.vector(hmm$distributions[,'mean']), # double* initial_means
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+			variances.initial <- as.vector(hmm$distributions[,'variance']), # double* initial_variances
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+			A.initial <- as.vector(hmm$A), # double* initial_A
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+			proba.initial <- as.vector(hmm$proba), # double* initial_proba
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+			use.initial.params <- as.logical(1), # bool* use_initial_params
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+			num.threads <- as.integer(num.threads) # int* num_threads
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+		)
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+	}
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+
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+	hmm$coordinates <- binned.data[,coordinatenames[1:3]]
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+	hmm$posteriors <- matrix(hmm$posteriors, ncol=hmm$num.states)
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+	colnames(hmm$posteriors) <- paste("P(",statelabels,")", sep="")
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+	class(hmm) <- "aneufinder.model"
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+	hmm$states <- statelabels[apply(hmm$posteriors, 1, which.max)]
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+	hmm$eps <- eps
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+	hmm$A <- matrix(hmm$A, ncol=hmm$num.states, byrow=TRUE)
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+	rownames(hmm$A) <- statelabels
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+	colnames(hmm$A) <- statelabels
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+	hmm$distributions <- cbind(mean=hmm$means, variance=hmm$variances, size=fsize(hmm$means,hmm$variances), prob=fprob(hmm$means,hmm$variances))
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+	rownames(hmm$distributions) <- statelabels
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+	hmm$A.initial <- matrix(hmm$A.initial, ncol=hmm$num.states, byrow=TRUE)
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+	rownames(hmm$A.initial) <- statelabels
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+	colnames(hmm$A.initial) <- statelabels
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+	hmm$distributions.initial <- cbind(mean=hmm$means.initial, variance=hmm$variances.initial, size=fsize(hmm$means.initial,hmm$variances.initial), prob=fprob(hmm$means.initial,hmm$variances.initial))
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+	rownames(hmm$distributions.initial) <- statelabels
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+	if (num.trials == 1) {
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+		if (hmm$loglik.delta > hmm$eps) {
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+			war <- warning("HMM did not converge!\n")
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+		}
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+	}
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+
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+	if (!is.null(war)) {
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+		if (output.if.not.converged == TRUE) {
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+			return(hmm)
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+		}
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+	} else {
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+		return(hmm)
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+	}
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+}
R/get.states.R
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-get.states <- function(model, threshold=0.5, separate.zeroinflation=FALSE) {
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-	numbins <- nrow(model$posteriors)
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-	states <- rep(NA,numbins)
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-	if (is(model,"chromStar.univariate.model")) {
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-		if (separate.zeroinflation) {
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-			states[ model$posteriors[,3]<=threshold & model$posteriors[,2]<=model$posteriors[,1] ] <- 1
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-			states[ model$posteriors[,3]<=threshold & model$posteriors[,2]>=model$posteriors[,1] ] <- 2
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-			states[ model$posteriors[,3]>threshold ] <- 3
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-			states <- as.factor(c("zeroinflation","unmodified","modified"))[states]
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-		} else {
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-			states <- ifelse(model$posteriors[,3]>threshold, 2, 1)
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-			states <- as.factor(c("unmodified","modified"))[states]
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-		}
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-		return(states)
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-	} else if (is(model,"chromStar.multivariate.model")) {
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-		states <- model$stateorder[apply(model$posteriors, 1, which.max)]
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-		return(states)
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-	} else {
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-		stop("Supply either a univariate or multivariate chromStar model")
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-	}
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-
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-}
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-
R/helper.R
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+fsize <- function(mean, variance) {
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+	return(mean^2 / (variance - mean))
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+}
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+
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+fprob <- function(mean, variance) {
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+	return(mean/variance)
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+}
R/gtools.R
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similarity index 100%
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rename from R/gtools.R
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rename to R/mixedsort.R
R/univariate.from.binned.data.R
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-univariate.from.binned.data = function(binned.data, eps=0.001, max.time=-1, max.it=-1, num.trials=1, eps.try=NULL, num.threads=1, output.if.not.converged=FALSE, filter.reads=TRUE) {
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-
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-	## Intercept user input
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-	if (check.positive(eps)!=0) stop("argument 'eps' expects a positive numeric")
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-	if (check.integer(max.time)!=0) stop("argument 'max.time' expects an integer")
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-	if (check.integer(max.it)!=0) stop("argument 'max.it' expects an integer")
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-	if (check.positive.integer(num.trials)!=0) stop("argument 'num.trials' expects a positive integer")
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-	if (!is.null(eps.try)) {
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-		if (check.positive(eps.try)!=0) stop("argument 'eps.try' expects a positive numeric")
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-	}
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-	if (check.positive.integer(num.threads)!=0) stop("argument 'num.threads' expects a positive integer")
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-	if (check.logical(output.if.not.converged)!=0) stop("argument 'output.if.not.converged' expects a logical (TRUE or FALSE)")
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-
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-
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-	war = NULL
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-	if (is.null(eps.try)) eps.try = eps
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-
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-	coordinatenames = c("chrom","start","end","reads")
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-	names(binned.data) = coordinatenames
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-
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-	## Assign variables
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-	# number of states
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-	numstates = 3
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-	# prepare densities to use
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-	densityNames = c("ZI", "NB", "NB")
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-	enum = c("ZI","NB","ZINB","Other")
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-	densityNames = factor(densityNames, levels=enum)
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-	densityNames = as.numeric(densityNames) - 1
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-	# length of observation sequence
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-	numbins = length(binned.data$reads)
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-
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-	# Check if there are reads in the data, otherwise HMM will blow up
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-	if (!any(binned.data$reads!=0)) {
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-		stop("All reads in data are zero. No univariate HMM done.")
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-	}
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-
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-	# Filter high reads out, makes HMM faster
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-	if (filter.reads) {
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-		limit <- 10*ceiling(var(binned.data$reads))
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-		mask <- binned.data$reads > limit
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-		binned.data$reads[mask] <- limit
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-		numfiltered <- length(which(mask))
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-		if (numfiltered > 0) {
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-			warning(paste("There are very high read counts (probably artificial) in your data. Replaced read counts > ",limit," (10*variance) by ",limit," in ",numfiltered," bins. Set option 'filter.reads=FALSE' to disable this filtering.", sep=""))
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-		}
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-	}
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-
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-
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-	## Call univariate in a for loop to enable multiple trials
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-	modellist = list()
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-	for (i_try in 1:num.trials) {
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-		cat("\n\nTry ",i_try," of ",num.trials," ------------------------------\n")
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-		z = .C("R_univariate_hmm",
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-			as.integer(binned.data$reads), # double* O
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-			as.integer(numbins), # int* T
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-			as.integer(numstates), # int* N
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-			as.integer(densityNames), # I pass them but they are fixed
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-			double(length=numstates), # double* r
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-			double(length=numstates), # double* p
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-			as.integer(max.it), #  int* maxiter
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-			as.integer(max.time), # double* maxtime
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-			as.double(eps.try), # double* eps
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-			double(length=numbins * numstates), # double* post
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-			double(length=numstates*numstates), # double* A
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-			double(length=numstates), # double* proba
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-			double(length=1), # double* loglik
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-			double(length=numstates), # double* softweights
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-			double(length=numstates), # double* initial r
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-			double(length=numstates), # double* initial p
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-			double(length=numstates*numstates), # double* initial A
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-			double(length=numstates), # double* initial proba
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-			as.logical(0), # bool* use_initial_params
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-			as.integer(num.threads) # int* num_threads
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-		)
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-
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-		model = NULL
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-		model$loglik = z[[13]]
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-		model$iteration = z[[7]]
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-		model$time.in.sec = z[[8]]
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-		model$delta.loglik = z[[9]]
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-		model$epsilon = eps.try
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-		model$proba = z[[12]]
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-		model$A = matrix(z[[11]], ncol=numstates, byrow=TRUE)
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-		model$distributions = cbind(r=z[[5]], p=z[[6]])
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-		model$softweights = z[[14]]
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-		model$proba.initial = z[[18]]
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-		model$A.initial = matrix(z[[17]], ncol=numstates, byrow=TRUE)
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-		model$distributions.initial = cbind(r=z[[15]], p=z[[16]])
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-		model$num.threads = z[[20]]
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-		if (num.trials > 1) {
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-			if (model$delta.loglik > model$epsilon) {
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-				warning("HMM did not converge in trial run ",i_try,"!\n")
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-			}
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-			# Store model in list
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-			modellist[[i_try]] = model
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-		}
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-	}
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-
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-	if (num.trials > 1) {
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-
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-		# Select fit with best loglikelihood
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-		indexmax = which.max(unlist(lapply(modellist,"[[","loglik")))
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-		model = modellist[[indexmax]]
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-
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-		# Rerun the HMM with different epsilon and initial parameters from trial run
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-		cat("\n\nRerunning try ",indexmax," with eps =",eps,"--------------------\n")
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-		z = .C("R_univariate_hmm",
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-			as.integer(binned.data$reads), # double* O
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-			as.integer(numbins), # int* T
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-			as.integer(numstates), # int* N
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-			as.integer(densityNames), # I pass them but they are fixed
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-			double(length=numstates), # double* r
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-			double(length=numstates), # double* p
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-			as.integer(max.it), #  int* maxiter
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-			as.integer(max.time), # double* maxtime
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-			as.double(eps), # double* eps
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-			double(length=numbins * numstates), # double* post
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-			double(length=numstates*numstates), # double* A
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-			double(length=numstates), # double* proba
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-			double(length=1), # double* loglik
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-			double(length=numstates), # double* softweights
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-			as.vector(model$distributions[,'r']), # double* initial r
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-			as.vector(model$distributions[,'p']), # double* initial p
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-			as.vector(model$A), # double* initial A
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-			as.vector(model$proba), # double* initial proba
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-			as.logical(1), # bool* use_initial_params
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-			as.integer(num.threads) # int* num_threads
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-		)
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-	}
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-
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-	model = NULL
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-	model$coordinates = binned.data[,coordinatenames[1:3]]
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-	model$reads = binned.data$reads
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-	model$posteriors = matrix(z[[10]], ncol=numstates)
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-	colnames(model$posteriors) = c("P(state = zero inflation)","P(state = unmodified)","P(state = modified)")
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-	class(model) = "chromStar.univariate.model"
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-	model$states = get.states(model)
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-	model$states.with.zeroinflation = get.states(model, separate.zeroinflation=TRUE)
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-	model$loglik = z[[13]]
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-	model$iteration = z[[7]]
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-	model$time.in.sec = z[[8]]
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-	model$delta.loglik = z[[9]]
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-	model$epsilon = eps
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-	model$proba = z[[12]]
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-	model$A = matrix(z[[11]], ncol=numstates, byrow=TRUE)
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-	model$distributions = cbind(r=z[[5]], p=z[[6]])
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-	model$softweights = z[[14]]
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-	model$proba.initial = z[[18]]
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-	model$A.initial = matrix(z[[17]], ncol=numstates, byrow=TRUE)
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-	model$distributions.initial = cbind(r=z[[15]], p=z[[16]])
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-	model$num.threads = z[[20]]
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-	if (num.trials == 1) {
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-		if (model$delta.loglik > model$epsilon) {
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-			war = warning("HMM did not converge!\n")
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-		}
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-	}
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-
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-	if (!is.null(war)) {
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-		if (output.if.not.converged == TRUE) {
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-			return(model)
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-		}
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-	} else {
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-		return(model)
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-	}
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-}
README.md
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+!!!THIS PROJECT IS CURRENTLY BEING DEVELOPED!!!
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+It has not been tested so far and is likely to produce no or incorrect results.
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TODO.md
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+- update() only disomy and fix others to multiples
man/.svn/entries
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man/.svn/text-base/Zinba-class.Rd.svn-base
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1 
-\name{Zinba-class}
2 
-\Rdversion{1.1}
3 
-\docType{class}
4 
-\alias{Distribution-class}
5 
-\alias{LogNormal-class}
6 
-\alias{Zinba-class}
7 
-\alias{fit,Zinba,numeric,numeric-method}
8 
-\alias{logLikelihood,Zinba,numeric-method}
9 
-
10 
-\title{Class \code{Distribution}}
11 
-\description{
12 
-Classes to represent distributions in mixture models in particular to be
13 
-used with the \code{\link{em}} function. Currently Zinba is used but
14 
-LogNormal is also available.
15 
-}
16 
-\section{Objects from the Class Zinba}{
17 
-Objects can be created by calls of the form \code{new("Zinba",
18 
-  ...)}. For a definition of the density see \code{\link{dzinba}}.
19 
-}
20 
-\section{Slots}{
21 
-  \describe{
22 
-    \item{\code{size}:}{size parameter of the negative binomial distribution}
23 
-    \item{\code{mu}:}{mean parameter of the negative binomial distribution}
24 
-    \item{\code{beta}:}{zero inflation parameter}
25 
-  }
26 
-}
27 
-\section{Extends}{
28 
-Class \code{"\linkS4class{Distribution}"}, directly.
29 
-}
30 
-\section{Methods}{
31 
-  \describe{
32 
-    \item{fit}{\code{signature(model = "Zinba", x = "numeric",
33 
-	logExpectation = "numeric")}: used in the maximization step }
34 
-    \item{logLikelihood}{\code{signature(model = "Zinba", x =
35 
-	"numeric")}: used in the expectation step }
36 
-  }
37 
-}
38 
-
39 
-\section{Objects from the Class LogNormal}{
40 
-Objects can be created by calls of the form \code{new("LogNormal", ...)}.
41 
-}
42 
-\section{Slots}{
43 
-  \describe{
44 
-    \item{\code{meanlog}:}{mean parameter (log space)}
45 
-    \item{\code{sdlog}:}{standard deviation parameter (log space)}
46 
-  }
47 
-}
48 
-
49 
-
50 
-\author{
51 
-Matthias Heinig
52 
-}
53 
-
54 
-\seealso{
55 
-\code{\link{dzinba}}, \code{\link{dlnorm}}
56 
-}
57 
-\examples{
58 
-showClass("Zinba")
59 
-}
60 
-\keyword{classes}
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-\name{callRegions}
2 
-\alias{callRegions}
3 
-\title{
4 
-call regions from a posterior matrix
5 
-}
6 
-\description{
7 
-Call regions using a cutoff on the posterior probabilities.
8 
-}
9 
-\usage{
10 
-callRegions(avg.posterior, cutoff, posterior.col = "avg.posterior", expr.col = "avg.expression")
11 
-}
12 
-
13 
-\arguments{
14 
-  \item{avg.posterior}{
15 
-matrix or data.frame with state posterior probabilities
16 
-}
17 
-  \item{cutoff}{
18 
-cutoff on the posterior probability
19 
-}
20 
-  \item{posterior.col}{
21 
-column index or name of the posterior probability
22 
-}
23 
-  \item{expr.col}{
24 
-column index or name of the gene expression column
25 
-}
26 
-}
27 
-
28 
-\details{
29 
-  The function expects a data.frame or matrix containing the position
30 
-  of bins in the genome, the results of the HMM analysis and if
31 
-  available gene expression data:
32 
-  chrom (chromosome),
33 
-  start (start coordinate of the bin),
34 
-  end (end coordinate of the bin),
35 
-  posterior (posterior probability a HMM state)
36 
-  expr (expression values assigned to each bin)
37 
-
38 
-  The first 3 columns have to be named exactly as given above, the
39 
-  colnames of the posterior prob and expression data are taken from the
40 
-  arguments to the function.
41 
-
42 
-  The regions will be called by applying the specified cutoff and
43 
-  joining all consecutive bins. Posterior probabilities as well as
44 
-  expression values will be avaraged over the called region.
45 
-
46 
-}
47 
-\value{
48 
-A GRanges object with metadata columns
49 
-\item{avg.posterior}{Average state posterior in the region}
50 
-\item{avg.expr}{Average gene expression in the region}
51 
-
52 
-}
53 
-\references{
54 
-}
55 
-\author{
56 
-Matthias Heinig
57 
-}
58 
-\note{
59 
-}
60 
-
61 
-\seealso{
62 
-
63 
-}
64 
-\examples{
65 
-##---- Should be DIRECTLY executable !! ----
66 
-##-- ==>  Define data, use random,
67 
-##--	or do  help(data=index)  for the standard data sets.
68 
-
69 
-## The function is currently defined as
70 
-function (avg.posterior, cutoff, posterior.col = "avg.posterior",
71 
-    expr.col = "avg.expression")
72 
-{
73 
-    require(GenomicRanges)
74 
-    called.regions = avg.posterior[which(avg.posterior[, posterior.col] >
75 
-        cutoff), ]
76 
-    called.granges = GRanges(seqnames = called.regions$chrom,
77 
-        ranges = IRanges(start = called.regions$start, end = called.regions$end))
78 
-    elementMetadata(called.granges) = DataFrame(called.regions[,
79 
-        c(posterior.col, expr.col)])
80 
-    reduced.granges = reduce(called.granges)
81 
-    called2reduced = matchMatrix(findOverlaps(called.granges,
82 
-        reduced.granges))
83 
-    reduced.posterior = tapply(elementMetadata(called.granges)[,
84 
-        posterior.col], called2reduced[, 2], mean, na.rm = T)
85 
-    reduced.expression = tapply(elementMetadata(called.granges)[,
86 
-        expr.col], called2reduced[, 2], mean, na.rm = T)
87 
-    elementMetadata(reduced.granges) = DataFrame(avg.posterior = reduced.posterior,
88 
-        avg.expression = reduced.expression)
89 
-    return(reduced.granges)
90 
-  }
91 
-}
92 
-% Add one or more standard keywords, see file 'KEYWORDS' in the
93 
-% R documentation directory.
94 
-\keyword{ ~kwd1 }
95 
-\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
man/.svn/text-base/dzinba.Rd.svn-base
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1 
-\name{dzinba}
2 
-\alias{dzinba}
3 
-\alias{pzinba}
4 
-\alias{rzinba}
5 
-\title{
6 
-Zero inflated negative binomial distribution
7 
-}
8 
-\description{
9 
-  Density, CDF and random number generation for the zero inflated
10 
-  negative binomial distribution.
11 
-}
12 
-\usage{
13 
-dzinba(x, size, mu, beta, log = FALSE)
14 
-pzinba(x, size, mu, beta, lower.tail = TRUE)
15 
-rzinba(n, size, mu, beta)
16 
-}
17 
-\arguments{
18 
-  \item{x}{
19 
-count data
20 
-}
21 
-  \item{size}{
22 
-size parameter of the negative binomial distribution
23 
-}
24 
-  \item{mu}{
25 
-mean parameter of the negative binomial distribution
26 
-}
27 
-  \item{beta}{
28 
-zero inflation parameter
29 
-}
30 
-  \item{log}{
31 
-logical indicating whether to return log density values
32 
-}
33 
-\item{lower.tail}{
34 
-logical indicating whether to P(X<=k) or P(X>k)
35 
-}
36 
-\item{n}{
37 
-number of random samples from the distribution
38 
-}
39 
-}
40 
-\details{
41 
-  The density of the zero inflated negative binomial distribution is defined as
42 
-  P(x) = I(x == 0) * beta + I(x != 0) * (1 - beta) * dnbinom(x, size,
43 
-  mu).
44 
-}
45 
-\value{
46 
-dzinba is the density function, pzinba is the CDF, rzinba returns
47 
-\code{n} random samples from the distribution.
48 
-}
49 
-\references{
50 
-}
51 
-\author{
52 
-Matthias Heinig
53 
-}
54 
-
55 
-\seealso{
56 
-  \code{\link{fitzinba}}
57 
-}
58 
-\examples{
59 
-
60 
-x = rzinba(1000, mu=10, size=15, beta=0.1)
61 
-hist(x, breaks=50)
62 
-lines(0:max(x), dzinba(0:max(x), mu=10, size=15, beta=0.1) * length(x))
63 
-
64 
-}
65 
-% Add one or more standard keywords, see file 'KEYWORDS' in the
66 
-% R documentation directory.
67 
-\keyword{ distribution }
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1 
-\name{dzinba.copula}
2 
-\alias{dzinba.copula}
3 
-\alias{pzinba.copula}
4 
-\alias{rzinba.copula}
5 
-\title{
6 
-Density, CDF and random number generation from a bivariate zinba copula
7 
-}
8 
-\description{
9 
-Density, CDF and random number generation from a bivariate zinba copula
10 
-distribution
11 
-}
12 
-\usage{
13 
-dzinba.copula(x, y, fit, log = FALSE)
14 
-pzinba.copula(x, y, fit)
15 
-rzinba.copula(n, fit)
16 
-}
17 
-%- maybe also 'usage' for other objects documented here.
18 
-\arguments{
19 
-  \item{x}{
20 
-count data vector
21 
-}
22 
-  \item{y}{
23 
-count data vector
24 
-}
25 
-  \item{fit}{
26 
-parameters of the zinba copula distribution as returned by fit.zinba.copula:
27 
-  marginal.x (named parameter vector as returned by \code{\link{fitzinba}}),
28 
-  marginal.y (named parameter vector as returned by \code{\link{fitzinba}}),
29 
-  sigma (covariance matrix of the transformed variables) and
30 
-  mu (mean vector of the transformed variables)
31 
-}
32 
-  \item{log}{
33 
-logical indicating whether log density should be returned
34 
-}
35 
-  \item{n}{
36 
-number of random samples to be generated
37 
-}
38 
-}
39 
-\details{
40 
-dzinba.copula is the density function, zinba.copula is the CDF and
41 
-rzinba.copula returns n random samples from the distribution.
42 
-}
43 
-\value{
44 
-}
45 
-\references{
46 
-}
47 
-\author{
48 
-Matthias Heinig
49 
-}
50 
-\note{
51 
-}
52 
-
53 
-\seealso{
54 
-\code{\link{fit.zinba.copula}}, \code{\link{fitzinba}}
55 
-}
56 
-\examples{
57 
-
58 
-# define a zinba copula distribution
59 
-fit =  list(mu=c(0, 0),
60 
-  marginal.x=c(size=10, mu=10, beta=0.05),
61 
-  marginal.y=c(size=10, mu=20, beta=0.02),
62 
-  sigma=matrix(c(1, 0.5, 0.5, 1), ncol=2))
63 
-
64 
-# enumerate a 100x100 square
65 
-x = 0:60
66 
-y = 0:60
67 
-
68 
-# compute the density
69 
-d = t(sapply(x, function(xx) sapply(y, function(yy) dzinba.copula(xx, yy, fit))))
70 
-
71 
-# plot the density
72 
-image(x=x, y=y, z=log(d))
73 
-
74 
-}
75 
-% Add one or more standard keywords, see file 'KEYWORDS' in the
76 
-% R documentation directory.
77 
-\keyword{ distribution }
man/.svn/text-base/em.Rd.svn-base
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-\name{em}
2 
-\alias{em}
3 
-\alias{EMResult-class}
4 
-\title{
5 
-Expectation maximization algorithm
6 
-}
7 
-\description{
8 
-Expectation maximization algorithm for different classes of
9 
-distributions. Currently only the Zinba distribution is used, but
10 
-Lognormal is also available.
11 
-}
12 
-\usage{
13 
-em(x, ncomp, prop = NULL, maxit = 100, eps = 1e-04, model.constructor = "Zinba")
14 
-}
15 
-\arguments{
16 
-  \item{x}{
17 
-data vector
18 
-}
19 
-  \item{ncomp}{
20 
-number of mixture components
21 
-}
22 
-  \item{prop}{
23 
-initial guess of the mixture proportions
24 
-}
25 
-  \item{maxit}{
26 
-maximum number of iterations
27 
-}
28 
-  \item{eps}{
29 
-stop if the gain in loglikelihood is less than \code{eps}
30 
-}
31 
-\item{model.constructor}{
32 
-name of a constructor for the mixture components, can be "Zinba" or "Lognormal"
33 
-}
34 
-}
35 
-\details{
36 
-  If \code{prop} is NULL a uniform distribution is assumed.
37 
-}
38 
-\value{
39 
-  returns an object of class EMResult which has the following slots:
40 
-  \item{models}{a list containing the models for each component}
41 
-  \item{logLikelihood}{a matrix with the logLikelihood for each data
42 
-    point (rows) and each mixture component (cols) }
43 
-  \item{dataLogLikelihood}{the total log likelihood of the data}
44 
-  \item{proportions}{the mixture proportions}
45 
-}
46 
-\references{
47 
-}
48 
-\author{
49 
-Matthias Heinig
50 
-}
51 
-\note{
52 
-}
53 
-
54 
-\seealso{
55 
-The models are objects of the class \code{\link{Distribution}} for
56 
-example \code{\link{Zinba}} or \code{\link{Lognormal}}.
57 
-}
58 
-\examples{
59 
-x = rzinba(1000, size=10, mu=5, beta=0.1)
60 
-y = rzinba(1000, size=10, mu=10, beta=0.02)
61 
-
62 
-emfit = em(c(x, y), ncomp=2, model.constructor="Zinba")
63 
-
64 
-}
65 
-% Add one or more standard keywords, see file 'KEYWORDS' in the
66 
-% R documentation directory.
67 
-\keyword{ models }
68 
-
man/.svn/text-base/fit.zinba.copula.Rd.svn-base
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-\name{fit.zinba.copula}
2 
-\alias{fit.zinba.copula}
3 
-\title{
4 
-Maximum likelihood fit of the zinba copula distribution
5 
-}
6 
-\description{
7 
-Maximum likelihood fit of the 2 dimensional zinba copula distribution
8 
-}
9 
-\usage{
10 
-fit.zinba.copula(x, y, weight = 1, marginal.x = NULL, marginal.y = NULL)
11 
-}
12 
-%- maybe also 'usage' for other objects documented here.
13 
-\arguments{
14 
-  \item{x}{
15 
-count data vector
16 
-}
17 
-  \item{y}{
18 
-count data vector
19 
-}
20 
-  \item{weight}{
21 
-weight vector (can be used in the EM algorithm)
22 
-}
23 
-  \item{marginal.x}{
24 
-parameters of the marginal zinba distribution for \code{x}
25 
-}
26 
-  \item{marginal.y}{
27 
-parameters of the marginal zinba distribution for \code{y}
28 
-}
29 
-}
30 
-\details{
31 
-if \code{marginal.x} or \code{marginal.y} are NULL they will be
32 
-re-estimated, if they are given only the missing parameters will be
33 
-re-estimated. The format of the marginal parameters is explained below.
34 
-}
35 
-\value{
36 
-  a list with the parameters of the two marginal distributions:
37 
-  \item{marginal.x}{named parameter vector as returned by
38 
-    \code{\link{fitzinba}}}
39 
-  \item{marginal.y}{named parameter vector as returned by
40 
-    \code{\link{fitzinba}}}
41 
-  \item{sigma}{covariance matrix of the transformed variables}
42 
-  \item{mu}{mean vector of the transformed variables}
43 
-}
44 
-\references{
45 
-}
46 
-\author{
47 
-Matthias Heinig
48 
-}
49 
-\note{
50 
-}
51 
-
52 
-\seealso{
53 
-\code{\link{fitzinba}}, \code{\link{dzinba.copula}}
54 
-}
55 
-\examples{
56 
-x = rzinba.copula(1000, list(mu=c(0, 0), marginal.x=c(size=10, mu=10, beta=0.1), marginal.y=c(size=10, mu=20, beta=0.02), sigma=matrix(c(1, 0.5, 0.5, 1), ncol=2)))
57 
-
58 
-fit = fit.zinba.copula(x[,1], x[,2])
59 
-
60 
-}
61 
-% Add one or more standard keywords, see file 'KEYWORDS' in the
62 
-% R documentation directory.
man/.svn/text-base/fitzinba.Rd.svn-base
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-\name{fitzinba}
2 
-\alias{fitzinba}
3 
-
4 
-\title{
5 
-Fit parameters of a zero inflated negative binomial distribution
6 
-}
7 
-\description{
8 
-Fit parameters of a zero inflated negative binomial distribution
9 
-}
10 
-\usage{
11 
-fitzinba(x, weight = 1)
12 
-}
13 
-\arguments{
14 
-  \item{x}{vector of count data}
15 
-  \item{weight}{weight of each data point (used in the EM algorithm)}
16 
-}
17 
-\details{
18 
-  The density of the zero inflated negative binomial distribution is defined as
19 
-  P(x) = I(x == 0) * beta + I(x != 0) * (1 - beta) * dnbinom(x, size,
20 
-  mu). The maximum likelihood estimate of the parameters is found using
21 
-  numerical optimization (see \code{\link{optim}}).
22 
-}
23 
-\value{
24 
-  Returns a vector with three named values:
25 
-  \item{size}{size parameter}
26 
-  \item{mu}{mean parameter}
27 
-  \item{beta}{zero inflation parameter}
28 
-}
29 
-\references{
30 
-}
31 
-\author{
32 
-Matthias Heinig
33 
-}
34 
-
35 
-\seealso{
36 
-\code{\link{dzinba}}, \code{\link{pzinba}}, \code{\link{rzinba}}
37 
-}
38 
-\examples{
39 
-x = rzinba(n=1000, beta=0.1, mu=10, size=15)
40 
-fitzinba(x)
41 
-}
42 
-% Add one or more standard keywords, see file 'KEYWORDS' in the
43 
-% R documentation directory.
44 
-\keyword{distribution}
45 
-
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-\name{histoneHMM-package}
2 
-\alias{histoneHMM-package}
3 
-\alias{histoneHMM}
4 
-\docType{package}
5 
-\title{Analysis of histone modification data using a HMM}
6 
-\description{
7 
-  histoneHMM is a software to analyse ChIP-seq data of histone modifications with broad genomic footprints like H3K27me3. It allows for calling modified regions in single samples as well as for calling differentially modified regions in a comparison of two samples.
8 
-}
9 
-\details{
10 
-\tabular{ll}{
11 
-Package: \tab histoneHMM\cr
12 
-Type: \tab Package\cr
13 
-Version: \tab 1.0\cr
14 
-Date: \tab 2011-11-18\cr
15 
-License: \tab What license is it under?\cr
16 
-LazyLoad: \tab yes\cr
17 
-}
18 
-
19 
-The most important functions of the package are
20 
-\code{\link{zinba.hmm.posterior}},
21 
-\code{\link{zinbacopula.hmm.posterior}},
22 
-\code{\link{em}},
23 
-\code{\link{fitzinba}} and
24 
-\code{\link{fit.zinba.copula}}.
25 
-
26 
-}
27 
-\author{
28 
-Author: Matthias Heinig and Maria Colome Tatche
29 
-Maintainer: Matthias Heinig <heinig@molgen.mpg.de>
30 
-}
31 
-\references{http://histonehmm.molgen.mpg.de}
32 
-\keyword{ package }
33 
-\seealso{
34 
-}
35 
-\examples{
36 
-
37 
-# define model parameters for two states
38 
-mu = c(5, 10)
39 
-size = c(10, 10)
40 
-beta = c(0.1, 0.02)
41 
-
42 
-# generate observation sequences
43 
-x = c(rzinba(1000, mu=mu[1], size=size[1], beta=beta[1]),
44 
-  rzinba(1000, mu=mu[2], size=size[2], beta=beta[2]),
45 
-  rzinba(1000, mu=mu[1], size=size[1], beta=beta[1]))
46 
-
47 
-# get the posteriors
48 
-posterior = zinba.hmm.posterior(x, mu, size, beta)
49 
-
50 
-# plot the sequence with state assignments
51 
-plot(x, col=apply(posterior, 1, which.max))
52 
-
53 
-}
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-\name{hmm.posterior}
2 
-\alias{hmm.posterior}
3 
-%- Also NEED an '\alias' for EACH other topic documented here.
4 
-\title{
5 
-  HMM posterior
6 
-}
7 
-\description{
8 
-  compute the posterior probability of a lognormal emission hmm
9 
-}
10 
-\usage{
11 
-hmm.posterior(x, meanlog, sdlog, max.it=500, eps=1e-4, rho=0)
12 
-}
13 
-%- maybe also 'usage' for other objects documented here.
14 
-\arguments{
15 
-  \item{x}{
16 
-    observation vector, can be also a two row matrix with bivariate
17 
-    observations
18 
-  }
19 
-  \item{meanlog}{
20 
-    vector holding the means of the lognormal emisson distributions, can
21 
-    be also a two row matrix with mean vectors in the columns
22 
-  }
23 
-  \item{sdlog}{
24 
-    vector holding the sd of the lognormal emisson distributions, can be
25 
-    also a two row matrix with the sqrt of the diagonal elements of sigma
26 
-  }
27 
-  \item{max.it} {
28 
-    maximum number of iterations
29 
-  }
30 
-  \item{eps} {
31 
-    stop iterations if the gain in log likelihood is less than \code{eps}
32 
-  }
33 
-  \item{rho}{
34 
-    if the bivariate model is used this is a vector of correlation
35 
-    coefficients for each state
36 
-  }
37 
-}
38 
-\details{
39 
-  posterior decoding
40 
-}
41 
-\value{
42 
- matrix (T x N) of posterior probabilities
43 
-}
44 
-\references{
45 
-  Rabiner et al
46 
-}
47 
-\author{
48 
-  Matthias Heinig
49 
-}
50 
-\note{
51 
-}
52 
-
53 
-\seealso{
54 
-\code{\link{zinba.hmm.posterior}}
55 
-}
56 
-\examples{