deleted file mode 100644

@@ -1,136 +0,0 @@

-\name{sample.CNSetLM}

-\alias{sample.CNSetLM}

-\docType{data}

-\title{

-	Dataset of class 'CNSetLM'

-}

-\description{

-

-	The data for 2119 polymorphic and nonpolymorphic markers on

-	chromosome 1 for the CEPH and Yoruban HapMap samples.

-

-}

-\usage{data(sample.CNSetLM)}

-\format{

-

-	This class has been deprecated.  See example below for how to

-	update an existing 'CNSetLM' object to class 'CNSet'.

-

-	The data illustrates the \code{CNSetLM-class}, with

-	\code{assayData} containing the quantile-normalized

-	intensities for the A and B alleles, genotype calls and

-	confidence scores (call and callProbability), and

-	allele-specific copy number (CA and CB). The parameters from

-	the linear model are stored in the lM slot.

-

-}

-\examples{

-## class CNSetLM has been deprecated

-data(sample.CNSetLM)

-## update to class CNSet

-cnSet <- as(sample.CNSetLM, "CNSet")

-all(isCurrent(cnSet)) ## is the cnSet object current?

-##subsetting

-cnSet2 <- cnSet[, 1:5]

-stopifnot(batchNames(cnSet2) == "C")

-\dontrun{

-	## updating class CNSetLM using ff objects

-	## a bigger object with multiple batches

-	if(require(ff)){

-		outdir <- "/amber1/scratch/rscharpf/jss/hapmap2"

-		load(file.path(outdir, "container.rda"))

-		container <- object; rm(object); gc()

-		container2 <- as(container, "CNSet")

-		all(isCurrent(container2))

-		## test replacement methods, subset methods

-		table(batch(container2))

-		##generates warning ... would need open, close in the '[' method

-		invisible(open(nuA(container2)))

-		xx <- nu(container2, "A")[1:5, ]

-		nuA(container2)[1:5, ] <- xx

-		invisible(close(nuA(container2)))

-	}

-}

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

-## accessors for the feature-level info

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

-chromosome(cnSet)[1:5]

-position(cnSet)[1:5]

-isSnp(cnSet)[1:5]

-## 980 nonpolymorphic markers and 1139 polymoprhic markers

-table(isSnp(cnSet))

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

-## sample-level statistics computed by crlmm

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

-varLabels(cnSet)

-## accessors for sample-level statistics

-## The signal to noise ratio (SNR)

-cnSet$SNR[1:5]

-## the skew

-cnSet$SKW[1:5]

-## the gender (gender is imputed unless specified in the call to crlmm)

-table(cnSet$gender)  ## 1=male, 2=female

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

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

-##

-## accessors for parameters estimated from the linear model for copy

-## number (note that the parameters have dimension R x C, where R

-## corresponds to the number of features and C corresponds to the

-## number of batches)

-## -------------------------------------------------- estimate of

-## background

-dim(nu(cnSet, "A"))

-## background for the A allele in the 2 batches for the

-## first 5 markers

-nu(cnSet, "A")[1:5, ]

-## background for the B allele in the 2 batches for the

-## first 5 markers

-nu(cnSet, "B")[1:5, ]

-## the slope

-phi(cnSet, "A")[1:5, ]

-## correlation within genotype cluster AA

-##corr(cnSet, "AA")[1:5, ]

-#### correlation within genotype cluster AB

-##corr(cnSet, "AB")[1:5, ]

-#### correlation within genotype cluster BB

-##corr(cnSet, "BB")[1:5, ]

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

-

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

-## calculating allele-specific copy number

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

-## copy number for allele A, first 5 markers, first 2 samples

-(ca <- CA(cnSet, i=1:5, j=1:2))

-## copy number for allele B, first 5 markers, first 2 samples

-(cb <- CB(cnSet, i=1:5, j=1:2))

-## total copy number for first 5 markers, first 2 samples

-(cn1 <- ca+cb)

-

-## total copy number at first 5 nonpolymorphic loci

-index <- which(!isSnp(cnSet))[1:5]

-cn2 <- CA(cnSet, i=index, j=1:2)

-## note, cb is NA at nonpolymorphic loci

-(cb <- CB(cnSet, i=index, j=1:2))

-## note, ca+cb will give NAs at nonpolymorphic loci

-CA(cnSet, i=index, j=1:2) + cb

-## A shortcut for total copy number

-cn3 <- totalCopynumber(cnSet, i=1:5, j=1:2)

-all.equal(cn3, cn1)

-cn4 <- totalCopynumber(cnSet, i=index, j=1:2)

-all.equal(cn4, cn2)

-

-## markers 1-5, all samples

-cn5 <- totalCopynumber(cnSet, i=1:5)

-## all markers, samples 1-5

-cn6 <- totalCopynumber(cnSet, j=1:5)

-

-## NOTE: subsetting the object before extracting copy number

-##       can be very inefficient when the data set is very large,

-##       particularly if using ff objects.  IN particular, subsetting

-##       the CNSet object will subset all of the assay data elements

-##       and all of the elements in the LinearModelParameter slot

-\dontrun{

-	cnsubset <- cnSet[1:5, ]

-}

-}

-\keyword{datasets}

@@ -30,7 +30,9 @@ data(sample.CNSetLM)

 ## update to class CNSet

 cnSet <- as(sample.CNSetLM, "CNSet")

 all(isCurrent(cnSet)) ## is the cnSet object current?

-

+##subsetting

+cnSet2 <- cnSet[, 1:5]

+stopifnot(batchNames(cnSet2) == "C")

 \dontrun{

 	## updating class CNSetLM using ff objects

 	## a bigger object with multiple batches

@@ -31,28 +31,23 @@ data(sample.CNSetLM)

 cnSet <- as(sample.CNSetLM, "CNSet")

 all(isCurrent(cnSet)) ## is the cnSet object current?

-## updating class CNSetLM using ff objects

-library(ff)

-path <- system.file("extdata", package="crlmm")

-ldPath(path)

-data(sample.CNSetLMff)

-cnSetff <- as(sample.CNSetLMff, "CNSet")

-all(isCurrent(cnSet))

-

-## a bigger object with multiple batches

 \dontrun{

-	outdir <- "/amber1/scratch/rscharpf/jss/hapmap2"

-	load(file.path(outdir, "container.rda"))

-	container <- object; rm(object); gc()

-	container2 <- as(container, "CNSet")

-	all(isCurrent(container2))

-	## test replacement methods, subset methods

-	table(batch(container2))

-	##generates warning ... would need open, close in the '[' method

-	invisible(open(nuA(container2)))

-	xx <- nu(container2, "A")[1:5, ]

-	nuA(container2)[1:5, ] <- xx

-	invisible(close(nuA(container2)))

+	## updating class CNSetLM using ff objects

+	## a bigger object with multiple batches

+	if(require(ff)){

+		outdir <- "/amber1/scratch/rscharpf/jss/hapmap2"

+		load(file.path(outdir, "container.rda"))

+		container <- object; rm(object); gc()

+		container2 <- as(container, "CNSet")

+		all(isCurrent(container2))

+		## test replacement methods, subset methods

+		table(batch(container2))

+		##generates warning ... would need open, close in the '[' method

+		invisible(open(nuA(container2)))

+		xx <- nu(container2, "A")[1:5, ]

+		nuA(container2)[1:5, ] <- xx

+		invisible(close(nuA(container2)))

+	}

 }

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

 ## accessors for the feature-level info

@@ -36,7 +36,7 @@ library(ff)

 path <- system.file("extdata", package="crlmm")

 ldPath(path)

 data(sample.CNSetLMff)

-cnSet <- as(sample.CNSetLMff, "CNSet")

+cnSetff <- as(sample.CNSetLMff, "CNSet")

 all(isCurrent(cnSet))

 ## a bigger object with multiple batches

@@ -91,18 +91,12 @@ nu(cnSet, "A")[1:5, ]

 nu(cnSet, "B")[1:5, ]

 ## the slope

 phi(cnSet, "A")[1:5, ]

-## the variance for CN > 0 (log2-scale)

-sigma2(cnSet, "A")[1:5, ]

-sigma2(cnSet, "B")[1:5, ]

-## background variance (log2-scale)

-tau2(cnSet, "A")[1:5, ]

-tau2(cnSet, "B")[1:5, ]

 ## correlation within genotype cluster AA

-corr(cnSet, "AA")[1:5, ]

-## correlation within genotype cluster AB

-corr(cnSet, "AB")[1:5, ]

-## correlation within genotype cluster BB

-corr(cnSet, "BB")[1:5, ]

+##corr(cnSet, "AA")[1:5, ]

+#### correlation within genotype cluster AB

+##corr(cnSet, "AB")[1:5, ]

+#### correlation within genotype cluster BB

+##corr(cnSet, "BB")[1:5, ]

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

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

@@ -54,25 +54,6 @@ all(isCurrent(cnSet))

 	nuA(container2)[1:5, ] <- xx

 	invisible(close(nuA(container2)))

 }

-

-

-

-

-nms <- names(lM(sample.CNSetLMff))

-##names must be tau2A, tau2B, ...

-nms2 <- sapply(nms, function(x) strsplit(x, "\\.1")[[1]][[1]], USE.NAMES=FALSE)

-names(sample.CNSetLMff@lM) <- nms2

-cnSetff <- as(sample.CNSetLMff, "CNSet")

-## try replacement method without warning

-nuA(cnSetff)[1:10, ] <- matrix(1:10, 10, 1)

-

-## try subset method without warning

-

-

-

-## information on the features

-fvarLabels(cnSet)

-##

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

 ## accessors for the feature-level info

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

@@ -81,8 +62,6 @@ position(cnSet)[1:5]

 isSnp(cnSet)[1:5]

 ## 980 nonpolymorphic markers and 1139 polymoprhic markers

 table(isSnp(cnSet))

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

-

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

 ## sample-level statistics computed by crlmm

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

@@ -95,16 +74,14 @@ cnSet$SKW[1:5]

 ## the gender (gender is imputed unless specified in the call to crlmm)

 table(cnSet$gender)  ## 1=male, 2=female

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

-

-

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

-## accessors for linear model parameters estimated from

-## the linear model for copy number

-## (note that the parameters have dimension R x C, where

-##  R corresponds to the number of features and C corresponds

-##  to the number of batches)

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

-## estimate of background

+##

+## accessors for parameters estimated from the linear model for copy

+## number (note that the parameters have dimension R x C, where R

+## corresponds to the number of features and C corresponds to the

+## number of batches)

+## -------------------------------------------------- estimate of

+## background

 dim(nu(cnSet, "A"))

 ## background for the A allele in the 2 batches for the

 ## first 5 markers

@@ -146,15 +123,15 @@ cn2 <- CA(cnSet, i=index, j=1:2)

 ## note, ca+cb will give NAs at nonpolymorphic loci

 CA(cnSet, i=index, j=1:2) + cb

 ## A shortcut for total copy number

-cn3 <- totalCopyNumber(cnSet, i=1:5, j=1:2)

+cn3 <- totalCopynumber(cnSet, i=1:5, j=1:2)

 all.equal(cn3, cn1)

-cn4 <- totalCopyNumber(cnSet, i=index, j=1:2)

+cn4 <- totalCopynumber(cnSet, i=index, j=1:2)

 all.equal(cn4, cn2)

 ## markers 1-5, all samples

-cn5 <- totalCopyNumber(cnSet, i=1:5)

+cn5 <- totalCopynumber(cnSet, i=1:5)

 ## all markers, samples 1-5

-cn6 <- totalCopyNumber(cnSet, j=1:5)

+cn6 <- totalCopynumber(cnSet, j=1:5)

 ## NOTE: subsetting the object before extracting copy number

 ##       can be very inefficient when the data set is very large,

@@ -31,13 +31,13 @@ data(sample.CNSetLM)

 cnSet <- as(sample.CNSetLM, "CNSet")

 all(isCurrent(cnSet)) ## is the cnSet object current?

-library(crlmm)

+## updating class CNSetLM using ff objects

 library(ff)

-outdir <- "~/madman/Rpacks/crlmm/inst/extdata"

-ldPath(outdir)

+path <- system.file("extdata", package="crlmm")

+ldPath(path)

 data(sample.CNSetLMff)

-cnSetff <- as(sample.CNSetLMff, "CNSet")

-all(isCurrent(cnSetff))

+cnSet <- as(sample.CNSetLMff, "CNSet")

+all(isCurrent(cnSet))

 ## a bigger object with multiple batches

 \dontrun{

@@ -41,18 +41,18 @@ all(isCurrent(cnSetff))

 ## a bigger object with multiple batches

 \dontrun{

-outdir <- "/amber1/scratch/rscharpf/jss/hapmap2"

-load(file.path(outdir, "container.rda"))

-container <- object; rm(object)

-container2 <- as(container, "CNSet")

-all(isCurrent(container2))

-## test replacement methods, subset methods

-table(batch(container2))

-##generates warning ... would need open, close in the '[' method

-invisible(open(nuA(container2)))

-xx <- nu(container2, "A")[1:5, ]

-nuA(container2)[1:5, ] <- xx

-invisible(close(nuA(container2)))

+	outdir <- "/amber1/scratch/rscharpf/jss/hapmap2"

+	load(file.path(outdir, "container.rda"))

+	container <- object; rm(object); gc()

+	container2 <- as(container, "CNSet")

+	all(isCurrent(container2))

+	## test replacement methods, subset methods

+	table(batch(container2))

+	##generates warning ... would need open, close in the '[' method

+	invisible(open(nuA(container2)))

+	xx <- nu(container2, "A")[1:5, ]

+	nuA(container2)[1:5, ] <- xx

+	invisible(close(nuA(container2)))

 }

@@ -31,6 +31,45 @@ data(sample.CNSetLM)

 cnSet <- as(sample.CNSetLM, "CNSet")

 all(isCurrent(cnSet)) ## is the cnSet object current?

+library(crlmm)

+library(ff)

+outdir <- "~/madman/Rpacks/crlmm/inst/extdata"

+ldPath(outdir)

+data(sample.CNSetLMff)

+cnSetff <- as(sample.CNSetLMff, "CNSet")

+all(isCurrent(cnSetff))

+

+## a bigger object with multiple batches

+\dontrun{

+outdir <- "/amber1/scratch/rscharpf/jss/hapmap2"

+load(file.path(outdir, "container.rda"))

+container <- object; rm(object)

+container2 <- as(container, "CNSet")

+all(isCurrent(container2))

+## test replacement methods, subset methods

+table(batch(container2))

+##generates warning ... would need open, close in the '[' method

+invisible(open(nuA(container2)))

+xx <- nu(container2, "A")[1:5, ]

+nuA(container2)[1:5, ] <- xx

+invisible(close(nuA(container2)))

+}

+

+

+

+

+nms <- names(lM(sample.CNSetLMff))

+##names must be tau2A, tau2B, ...

+nms2 <- sapply(nms, function(x) strsplit(x, "\\.1")[[1]][[1]], USE.NAMES=FALSE)

+names(sample.CNSetLMff@lM) <- nms2

+cnSetff <- as(sample.CNSetLMff, "CNSet")

+## try replacement method without warning

+nuA(cnSetff)[1:10, ] <- matrix(1:10, 10, 1)

+

+## try subset method without warning

+

+

+

 ## information on the features

 fvarLabels(cnSet)

 ##

@@ -2,7 +2,7 @@

 \alias{sample.CNSetLM}

 \docType{data}

 \title{

-	Dataset of class 'CNSetLM'	

+	Dataset of class 'CNSetLM'

 }

 \description{

@@ -15,20 +15,115 @@

 	This class has been deprecated.  See example below for how to

 	update an existing 'CNSetLM' object to class 'CNSet'.

- 

+

 	The data illustrates the \code{CNSetLM-class}, with

 	\code{assayData} containing the quantile-normalized

 	intensities for the A and B alleles, genotype calls and

 	confidence scores (call and callProbability), and

 	allele-specific copy number (CA and CB). The parameters from

 	the linear model are stored in the lM slot.

-	

+

 }

 \examples{

 ## class CNSetLM has been deprecated

 data(sample.CNSetLM)

 ## update to class CNSet

 cnSet <- as(sample.CNSetLM, "CNSet")

-all(isCurrent(cnSet))

+all(isCurrent(cnSet)) ## is the cnSet object current?

+

+## information on the features

+fvarLabels(cnSet)

+##

+## --------------------------------------------------

+## accessors for the feature-level info

+## --------------------------------------------------

+chromosome(cnSet)[1:5]

+position(cnSet)[1:5]

+isSnp(cnSet)[1:5]

+## 980 nonpolymorphic markers and 1139 polymoprhic markers

+table(isSnp(cnSet))

+## --------------------------------------------------

+

+## --------------------------------------------------

+## sample-level statistics computed by crlmm

+## --------------------------------------------------

+varLabels(cnSet)

+## accessors for sample-level statistics

+## The signal to noise ratio (SNR)

+cnSet$SNR[1:5]

+## the skew

+cnSet$SKW[1:5]

+## the gender (gender is imputed unless specified in the call to crlmm)

+table(cnSet$gender)  ## 1=male, 2=female

+## --------------------------------------------------

+

+

+## --------------------------------------------------

+## accessors for linear model parameters estimated from

+## the linear model for copy number

+## (note that the parameters have dimension R x C, where

+##  R corresponds to the number of features and C corresponds

+##  to the number of batches)

+## --------------------------------------------------

+## estimate of background

+dim(nu(cnSet, "A"))

+## background for the A allele in the 2 batches for the

+## first 5 markers

+nu(cnSet, "A")[1:5, ]

+## background for the B allele in the 2 batches for the

+## first 5 markers

+nu(cnSet, "B")[1:5, ]

+## the slope

+phi(cnSet, "A")[1:5, ]

+## the variance for CN > 0 (log2-scale)

+sigma2(cnSet, "A")[1:5, ]

+sigma2(cnSet, "B")[1:5, ]

+## background variance (log2-scale)

+tau2(cnSet, "A")[1:5, ]

+tau2(cnSet, "B")[1:5, ]

+## correlation within genotype cluster AA

+corr(cnSet, "AA")[1:5, ]

+## correlation within genotype cluster AB

+corr(cnSet, "AB")[1:5, ]

+## correlation within genotype cluster BB

+corr(cnSet, "BB")[1:5, ]

+## --------------------------------------------------

+

+## --------------------------------------------------

+## calculating allele-specific copy number

+## --------------------------------------------------

+## copy number for allele A, first 5 markers, first 2 samples

+(ca <- CA(cnSet, i=1:5, j=1:2))

+## copy number for allele B, first 5 markers, first 2 samples

+(cb <- CB(cnSet, i=1:5, j=1:2))

+## total copy number for first 5 markers, first 2 samples

+(cn1 <- ca+cb)

+

+## total copy number at first 5 nonpolymorphic loci

+index <- which(!isSnp(cnSet))[1:5]

+cn2 <- CA(cnSet, i=index, j=1:2)

+## note, cb is NA at nonpolymorphic loci

+(cb <- CB(cnSet, i=index, j=1:2))

+## note, ca+cb will give NAs at nonpolymorphic loci

+CA(cnSet, i=index, j=1:2) + cb

+## A shortcut for total copy number

+cn3 <- totalCopyNumber(cnSet, i=1:5, j=1:2)

+all.equal(cn3, cn1)

+cn4 <- totalCopyNumber(cnSet, i=index, j=1:2)

+all.equal(cn4, cn2)

+

+## markers 1-5, all samples

+cn5 <- totalCopyNumber(cnSet, i=1:5)

+## all markers, samples 1-5

+cn6 <- totalCopyNumber(cnSet, j=1:5)

+

+## NOTE: subsetting the object before extracting copy number

+##       can be very inefficient when the data set is very large,

+##       particularly if using ff objects.  IN particular, subsetting

+##       the CNSet object will subset all of the assay data elements

+##       and all of the elements in the LinearModelParameter slot

+\dontrun{

+	cnsubset <- cnSet[1:5, ]

+}

 }

 \keyword{datasets}

@@ -13,6 +13,9 @@

 \usage{data(sample.CNSetLM)}

 \format{

+	This class has been deprecated.  See example below for how to

+	update an existing 'CNSetLM' object to class 'CNSet'.

+ 

 	The data illustrates the \code{CNSetLM-class}, with

 	\code{assayData} containing the quantile-normalized

 	intensities for the A and B alleles, genotype calls and

@@ -22,6 +25,10 @@

 }

 \examples{

+## class CNSetLM has been deprecated

 data(sample.CNSetLM)

+## update to class CNSet

+cnSet <- as(sample.CNSetLM, "CNSet")

+all(isCurrent(cnSet))

 }

 \keyword{datasets}

@@ -13,7 +13,7 @@

 \usage{data(sample.CNSetLM)}

 \format{

-	The data illustrates the \code{\link{CNSetLM-class}}, with

+	The data illustrates the \code{CNSetLM-class}, with

 	\code{assayData} containing the quantile-normalized

 	intensities for the A and B alleles, genotype calls and

 	confidence scores (call and callProbability), and

new file mode 100644

@@ -0,0 +1,27 @@

+\name{sample.CNSetLM}

+\alias{sample.CNSetLM}

+\docType{data}

+\title{

+	Dataset of class 'CNSetLM'	

+}

+\description{

+

+	The data for 2119 polymorphic and nonpolymorphic markers on

+	chromosome 1 for the CEPH and Yoruban HapMap samples.

+

+}

+\usage{data(sample.CNSetLM)}

+\format{

+

+	The data illustrates the \code{\link{CNSetLM-class}}, with

+	\code{assayData} containing the quantile-normalized

+	intensities for the A and B alleles, genotype calls and

+	confidence scores (call and callProbability), and

+	allele-specific copy number (CA and CB). The parameters from

+	the linear model are stored in the lM slot.

+	

+}

+\examples{

+data(sample.CNSetLM)

+}

+\keyword{datasets}