# R/sra.R In SuperRanker: Sequential Rank Agreement

#### Documented in random_list_srasmooth_srasrasra.defaultsra.listsra.matrixtest_sra

```#' Compute the sequential rank agreement
#'
#' @param object Either matrix where each column is a ranked list of
#'     items or a list of ranked lists of items. Elements are integers
#'     between 1 and the length of the lists. The lists should have
#'     the same length but censoring can be used by setting the list
#'     to zero from a point onwards. See details for more information.
#' @param B An integer giving the number of randomization to sample
#'     over in the case of censored observations
#' @param na.strings A vector of strings/values that represent missing
#'     values in addition to NA. Defaults to NULL which means only NA
#'     are censored values.
#' @param nitems The total number of items in the original lists if we only have partial lists available.
#' @param type The type of measure to use. Either sd (standard
#'     deviation - the default) or mad (median absolute deviance around the median)
#' @param epsilon A non-negative numeric vector that contains the minimum limit in proportion of lists that must show the item. Defaults to 0. If a single number is provided then the value will be recycles to the number of items.
#' @param ... Arguments passed to methods.
#' @return A vector of the sequential rank agreement
#' @examples
#'
#' mlist <- matrix(cbind(1:8,c(1,2,3,5,6,7,4,8),c(1,5,3,4,2,8,7,6)),ncol=3)
#' sra(mlist)
#'
#' mlist <- matrix(cbind(1:8,c(1,2,3,5,6,7,4,8),c(1,5,3,4,2,8,7,6)),ncol=3)
#' sra(mlist, nitems=20, B=10)
#'
#' alist <- list(a=1:8,b=sample(1:8),c=sample(1:8))
#' sra(alist)
#'
#' blist <- list(x1=letters,x2=sample(letters),x3=sample(letters))
#' sra(blist)
#'
#' ## censored lists are either too short
#' clist <- list(x1=c("a","b","c","d","e","f","g","h"),
#'               x2=c("h","c","f","g","b"),
#'               x3=c("d","e","a"))
#' set.seed(17)
#' sra(clist,na.strings="z",B=10)
#'
#' ## or use a special code for missing elements
#' Clist <- list(x1=c("a","b","c","d","e","f","g","h"),
#'               x2=c("h","c","f","g","b","z","z","z"),
#'               x3=c("d","e","a","z","z","z","z","z"))
#' set.seed(17)
#' sra(Clist,na.strings="z",B=10)
#'
#' @author Claus Ekstrøm <ekstrom@@sund.ku.dk> and Thomas A Gerds <tag@@biostat.ku.dk>
#'
#' @rdname sra
#' @export
sra <- function(object,B,na.strings,nitems,type,epsilon=0,...) {
UseMethod("sra")
}

#' @rdname sra
#' @export
sra.default <- function(object,B,na.strings,nitems,type,epsilon=0,...) {
stop("Input must be either a matrix, a data.frame or a list.")
}

#' @rdname sra
#' @export
sra.matrix <- function(object, B=1, na.strings=NULL, nitems=nrow(object), type=c("sd", "mad"),epsilon=0,...) {
if (!is.matrix(object))
stop("Input object must be a matrix")

## Convert all missing types to NAs
if (!is.null(na.strings)) {
object[object %in% na.strings] <- NA
}

unique.items <- length(unique(object[!is.na(object)]))

## Check that we dont have more unique values than rows
if (unique.items > nitems) {
stop("Found more unique items in the matrix than rows/nitems. Increase nitems to match")
}

## Expand the columns in the matrix to have length unique.items
if (nitems>nrow(object)) {
glue <- matrix(rep(NA, NCOL(object)*(nitems - nrow(object))), ncol=NCOL(object))
object <- rbind(object, glue)
}
object <- lapply(1:NCOL(object),function(j)object[,j]) # Convert matrix to list
sra.list(object, B=B, nitems=nitems, type=type, epsilon=epsilon)
}

#' @rdname sra
#' @export
sra.list <- function(object, B=1, na.strings=NULL, nitems=max(sapply(object, length)), type=c("sd", "mad"),epsilon=0,...) {
# Make sure that the input object ends up as a matrix with integer columns all
# consisting of elements from 1 and up to listlength

stopifnot(is.list(object))

nlists <- length(object)
nitems <- nitems ## force evaluation here

type <- match.arg(type)

## Sanity checks
object <- lapply(1:length(object),function(j){
x <- object[[j]]
## Add the NA items to be removed
out <- which(is.na(x))
if (!is.null(na.strings)) {
na.strings <- na.strings[!is.na(na.strings)]
out <- c(out,grep(paste0("^",na.strings,"\$"),x))
}
## remove censored items with side effect:
## in case where all lists have trailing censored information
## this is pruned
if (length(out)>0)
x <- x[-out]
## stop at duplicated items
if (any(duplicated(x)))
stop(paste0("Duplicated items found in list ",j))
x
})
## check class of elements, then coerce to integer

cc <- sapply(object, class)
if (length(cc <- unique(cc))>1)
stop(paste("All elements of object must have the same class. Found:",paste(cc,collapse=", ")))

if (match(cc,c("integer","character","numeric","factor"),nomatch=0)==0)
stop("Class of lists in object should be one of 'integer', 'character', 'numeric' or 'factor'.")

labels <- unique(unlist(object,recursive=TRUE,use.names=FALSE))

nitems <- max(nitems, length(labels))

object <- lapply(object,function(x){as.integer(factor(x,levels=labels))})

## items are coded as 1, 2, 3, ...
## missing items (sraNULL) are coded as 0
items <- seq(nitems)

## Compute a list of missing items for each list
missing.items <- lapply(object, function(x) {items[match(items,x,nomatch=0)==0]})
nmiss <- sapply(missing.items,length)

## fill too short lists with 0 (code for missing)
ll <- sapply(object,length)
listlength <- max(ll)
tooshort <- any(ll<nitems)
if (tooshort)
object <- lapply(object,function(x){c(x,rep(0,nitems-length(x)))})

## set B to 1, if there is no censoring
##             or if only one element is censored
iscensored <- any(nmiss!=0)
if (B!=1 && (!iscensored || (max(nmiss)==1))) {B <- 1}

itype <- 0
if (type == "mad")
itype <- 1

# Special version of sample needed
resample <- function(x, ...) x[sample.int(length(x), ...)]
tmpres <- sapply(1:B, function(b) {
obj.b <- lapply(1:nlists,function(j){
list <- object[[j]]
if (nmiss[[j]]>0){
list[list==0] <- resample(missing.items[[j]])
}
list
})
## bind lists
rankmat <- do.call("cbind",obj.b)
res <- sracppfull(rankmat, type=itype, epsilon=epsilon)\$sra
res
})
if (itype==0) {
agreement <- sqrt(rowMeans(tmpres))
} else {
agreement <- rowMeans(tmpres)
}
names(agreement) <- items
class(agreement) <- "sra"
attr(agreement, "B") <- B
attr(agreement, "type") <- type
attr(agreement, "epsilon") <- epsilon
if (B==1) {
# Refit once more to get the depths
obj.b <- lapply(1:nlists,function(j){
list <- object[[j]]
if (nmiss[[j]]>0){
list[list==0] <- resample(missing.items[[j]])
}
list
})
## bind lists
rankmat <- do.call("cbind",obj.b)

attr(agreement, "whenIncluded") <- sracppfull(rankmat, type=itype, epsilon=epsilon)\$whenIncluded
} else {
attr(agreement, "whenIncluded") <- NA
}
agreement
}

#' Simulate sequential rank agreement for randomized unrelated lists
#'
#' Simulate sequential rank agreement from completely uninformative lists (ie., raw permutations of items) and compute the corresponding sequential rank agreement curves.
#' The following attributes are copied from the input object: number of lists, number of items and amount of censoring.
#'
#' @param object A matrix of numbers or list of vectors representing ranked lists.
#' @param B An integer giving the number of randomizations to sample
#'     over in the case of censored observations
#' @param n Integer: the number of permutation runs. For each permutation run we permute each of the lists in object
#' and compute corresponding the sequential rank agreement curves
#' @param na.strings A vector of character values that represent
#'     censored observations
#' @param nitems The total number of items in the original lists if we only have partial lists available. Will be derived from the unique elements of the object if set to \code{NULL} (the default)
#' @param type The type of measure to use. Either sd (standard
#'     deviation - the default) or mad (median absolute deviance)
#' @param epsilon A non-negative numeric vector that contains the minimum limit in proportion of lists that must show the item. Defaults to 0. If a single number is provided then the value will be recycles to the number of items. Should usually be low.
#' @return A matrix with n columns and the same number of rows as for the input object. Each column contains one
#' simulated sequential rank agreement curve from one permutation run.
#' @author Claus Ekstrøm <ekstrom@@sund.ku.dk>
#' @examples
#' # setting with 3 lists
#' mlist <- matrix(cbind(1:8,c(1,2,3,5,6,7,4,8),c(1,5,3,4,2,8,7,6)),ncol=3)
#' # compute sequential rank agreement of lists
#' sra(mlist)
#' # compute sequential rank agreement of 5 random permutations
#' random_list_sra(mlist, n=5)
#'
#' @export
random_list_sra <- function(object, B=1, n=1, na.strings=NULL, nitems=NULL, type=c("sd", "mad"), epsilon=0) {

type <- match.arg(type)

## Make sure that the input object ends up as a matrix with integer columns all
## consisting of elements from 1 and up to listlength
if (is.list(object)) {

## Find largst length
largest <- max(sapply(object, function(i) { length(i)}))

## Unique non-missing elements
obj <- matrix(rep(NA, largest*length(object)), ncol=length(object))

for (i in 1:length(object)) {
obj[1:length(object[[i]]), i] <- object[[i]]
}

## Convert all missing types to NAs
if (!is.null(na.strings)) {
obj[obj %in% na.strings] <- NA
}
} else {
obj <- as.matrix(object)
}

listlengths <- nrow(obj)
if (is.null(nitems)) {
nitems <- 1
}
nitems <- max(nitems, NROW(obj), sum(!is.na(unique(as.vector(obj)))))

notmiss <- apply(obj, 2, function(x) {sum(!is.na(x))} )
res <- sapply(1:n, function(i) {
## Do a permutation with the same number of missing
for (j in 1:ncol(obj)) {
obj[,j] <- c(sample(nitems, size=notmiss[j]), rep(NA, listlengths-notmiss[j]))
}
sra(obj, B=B, nitems=nitems, type=type, epsilon=epsilon)
})
res

}

#' Smooth quantiles of a matrix of sequential ranked agreements.
#'
#' @param object A matrix
#' @param confidence the limits to compute
#' @return A list containing two vectors for the smoothed lower and upper limits
#' @author Claus Ekstrøm <ekstrom@@sund.ku.dk>
#' @examples
#' # setting with 3 lists
#' mlist <- matrix(cbind(1:8,c(1,2,3,5,6,7,4,8),c(1,5,3,4,2,8,7,6)),ncol=3)
#' # compute rank agreement of 5 random permutations
#' null=random_list_sra(mlist,n=15)
#' # now extract point-wise quantiles according to confidence level
#' smooth_sra(null)
#' @export
smooth_sra <- function(object, confidence=0.95) {

alpha <- (1-confidence)/2
limits <- apply(object, 1, function(x) {stats::quantile(x, probs=c(alpha, 1-alpha)) })
list(lower=limits[1,], upper=limits[2,])
}

#' Compute a Kolmogorov-Smirnoff-like test for Smooth quantiles of a matrix of sequential rank agreements
#'
#' @param object An object created with \code{sra}.
#' @param nullobject An object created with \code{random_list_sra}.
#' @param weights Either a single value or a vector of the same length as the number of item with the weight that should be given to specific depths.
#' @return A single value corresponding to the p-value
#' @author Claus Ekstrøm <ekstrom@@sund.ku.dk>
#' @examples
#' # setting with 3 lists
#' mlist <- matrix(cbind(1:8,c(1,2,3,5,6,7,4,8),c(1,5,3,4,2,8,7,6)),ncol=3)
#' # compute sequential rank agreements
#' x=sra(mlist)
#' # compute rank agreement of 5 random permutations
#' null=random_list_sra(mlist,n=15)
#' # now extract point-wise quantiles according to confidence level
#' test_sra(x,null)
#' # compare to when we use the result of the first permutation run
#' test_sra(null[,1],null[,-1])
#'
#' @export
test_sra <- function(object, nullobject, weights=1) {
## Sanity checks
if (! (length(weights) %in% c(1, length(object))))
stop("the vector of weights must have the same length as the number of items")

## Test statistic
T <- max(weights*abs(object - rowMeans(nullobject)))

## Now compute the individual jackknife variations from the null object
B <- ncol(nullobject)
nullres <- sapply(1:B, function(i) {
max(weights*abs(nullobject[,i] - rowMeans(nullobject[,-i])))
})

res <- sum(nullres>=T)/(B+1)
attr(res, "B") <- B
return(res)

}
```

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SuperRanker documentation built on Jan. 30, 2021, 1:06 a.m.