\name{SnpSet-methods}
\docType{methods}
\alias{calls}
\alias{calls,SnpSet-method}
\alias{calls<-}
\alias{calls<-,SnpSet,matrix-method}
\alias{confs}
\alias{confs,SnpSet-method}
\alias{confs<-}
\alias{confs<-,SnpSet,matrix-method}
\title{Accessors for Calls and Confidences on a SnpSet object}

\description{

  \code{calls} returns the genotype calls. CRLMM stores genotype calls
  as integers (1 - AA; 2 - AB; 3 - BB).

  \code{confs} returns the confidences associated with the genotype
  calls. The current implementation of CRLMM stores the confidences as
  integers to save memory on disk by using the transformation:

  round(-1000*log2(1-p)),

  where 'p' is the posterior probability of the call.  \code{confs} is
  a convenience function that transforms the integer representation
  back to a probability.  Note that if the assayData elements of the
  \code{SnpSet} objects are \code{ff_matrix} or \code{ffdf}, the
  \code{confs} function will return a warning. For such objects, one
  should first subset the \code{ff} object and coerce to a matrix,
  then apply the above conversion.  The function
  \code{snpCallProbability} for the \code{callProbability} slot of
  \code{SnpSet} objects.  See the examples below.


}

\section{Methods}{
	\describe{
	\item{\code{initialize(SnpSet)}:}{Object instantiation, used by
	  \code{new}; not to be called directly by the user.}
	\item{\code{calls(object)}:}{accessor for genotype calls}
	\item{\code{confs(object)}:}{accessor for crlmm genotype confidence scores}
	}
}
\seealso{\code{\link{addFeatureAnnotation}}, \code{\link{snpCallProbability}}}
\examples{

	theCalls <- matrix(sample(1:3, 20, rep=TRUE), nc=2)
	p <- matrix(runif(20), nc=2)
	integerRepresentation <- matrix(as.integer(round(-1000*log(1-p))), 10, 2)
	obj <- new("SnpSet", call=theCalls, callProbability=integerRepresentation)
	calls(obj)
	p2 <- confs(obj)
	dimnames(p2) <- NULL
	all.equal(p2, p)	## small differences due to rounding

	## example using ff
	if(require(ff)){
		ldPath(tempdir())
		integerRepresentation <- initializeBigMatrix("tmp", 10, 2)
		for(j in 1:2) integerRepresentation[, j] <- as.integer(round(-1000*log(1-p[, j])))
		integerRepresentation
		obj <- new("SnpSet", call=theCalls, callProbability=integerRepresentation)
		calls(obj)
		res <- tryCatch(confs(obj), error=function(e) NULL)
		is.null(res)
		integerRepresentation <- snpCallProbability(obj)
		##coerce to matrix by subsetting desired rows and columns (here we choose all rows and columns)
		integerRepresentation <- integerRepresentation[,]
		p3 <- oligoClasses:::i2p(integerRepresentation)
		dimnames(p3) <- NULL
		all.equal(p2, p3)
	}
}
\keyword{manip}