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#' @title Compute cumulative eigenvalue information
#'
#' @description Computes the information that is needed in order to make a \code{\link{CEPlot}}
#' from a \code{PCADSC} or \code{pcaRes} object. Typically, this function is called on a partial
#' \code{PCADSC} object in order to add \code{CEInfo} (see examples).
#'
#' @param x Either a \code{PCADSC} or a \code{pcaRes} object.
#'
#' @param ... If \code{doCE} is called on a \code{pcaRes} object, the full dataset must also
#' be supplied (as \code{data}), as well as the number of resampling steps (\code{B}).
#'
#' @examples
#' #load iris data
#' data(iris)
#'
#' #Define grouping variable, grouping the observations by whether their species is
#' #Setosa or not
#' iris$group <- "setosa"
#' iris$group[iris$Species != "setosa"] <- "non-setosa"
#' iris$Species <- NULL
#'
#' \dontrun{
#' #make a partial PCADSC object, splitting the data by "group"
#' irisPCADSC <- PCADSC(iris, "group", doCE = FALSE)
#'
#' #No CEInfo available
#' irisPCADSC$CEInfo
#'
#' #Add and show CEInfo
#' irisPCADSC <- doCE(irisPCADSC)
#' irisPCADSC$CEInfo
#' }
#'
#' #Make a partial PCADSC object and only add CE information with no
#' #bootstrapping (and thus no test)
#' irisPCADSC_fast <- PCADSC(iris, "group", doAngle = FALSE,
#' doChroma = FALSE, doCE = FALSE)
#' irisPCADSC_fast <- doCE(irisPCADSC_fast, B = 100)
#' irisPCADSC_fast$CEInfo
#'
#' @seealso \code{\link{CEPlot}}, \code{\link{PCADSC}}
#'
#' @export
doCE <- function(x, ...) {
UseMethod("doCE")
}
#x: pcaRes
#' @export
doCE.pcaRes <- function(x, data, B, ...) {
#check whether B is positive
if (B < 1) {
stop("B must be positive.")
}
#Unpack x
n1 <- x$n1
nBoth <- x$nBoth
d <- x$d
vars <- x$vars
#Calculate cumulative sums for the observed data
ceResObs <- calcCum(x)
xVals <- ceResObs$x
y.obs <- ceResObs$y
KS.obs <- ceResObs$KS
CvM.obs <- ceResObs$CvM
#Calculate cumulative sums for the randomly partitioned data
y.sim <- matrix(0, d+1, B)
KS.sim <- rep(0, B)
CvM.sim <- rep(0, B)
for (i in 1:B) {
splitVar <- rep("2", nBoth)
ii <- sample(1:nBoth, n1)
splitVar[ii] <- "1"
data$splitVar <- factor(splitVar)
thisCEres <- calcCum(doPCA(data, "splitVar", c("1", "2"), vars, doBoth = FALSE),
x = xVals)
y.sim[,i] <- thisCEres$y
KS.sim[i] <- thisCEres$KS
CvM.sim[i] <- thisCEres$CvM
}
#Calculate p-values
KS.pvalue <- mean(KS.sim >= KS.obs)
CvM.pvalue <- mean(CvM.sim >= CvM.obs)
#Pack and return output
out <- list(d = d, B = B, xVals = xVals, y.obs = y.obs, y.sim = y.sim,
KS.obs = KS.obs, KS.pvalue = KS.pvalue,
CvM.obs = CvM.obs, CvM.pvalue = CvM.pvalue)
class(out) <- "CEInfo"
out
}
#' @export
doCE.PCADSC <- function(x, ...) {
if ("B" %in% names(list(...))) b <- list(...)$B
else b <- x$B
x$CEInfo <- doCE(x$pcaRes, x$data, b)
x
}
################Not exported below##################################################
#Calculate cumulative sum of eigenvalue differences (and, if x is NULL, also
#just a cumulative eigenvalue sum for the joint eigenvalues)
calcCum <- function(pcaRes, x = NULL) {
eigen1 <- pcaRes$eigen1
eigen2 <- pcaRes$eigen2
if (is.null(x)) eigenBoth <- pcaRes$eigenBoth
if (is.null(x)) x <- c(0,cumsum(eigenBoth))
y <- c(0,cumsum(eigen1-eigen2))
KS <- max(abs(y))
CvM <- sum(x*(y^2))
list(x = x, y = y, KS = KS, CvM = CvM)
}
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