# R/CheckForOutliers.R In MetabolomicsBasics: Basic Functions to Investigate Metabolomics Data Matrices

#### Documented in CheckForOutliers

```#'@title CheckForOutliers.
#'
#'@description
#'\code{CheckForOutliers} will evaluate a numeric vector and check if outliers within groups based on group mean+-n*sd.
#'
#'@details
#'The numeric will be split by groups and each value will be evaluated with respect to its distance to the group mean
#'(calculated out of the other values in the group). Distance here means the number of standard deviations the value is off the group mean.
#'With different choices of \code{method} the output can be switched from the calculated fold-distances to a boolean of length(x) or
#'and Index vector giving the outliers directly (see examples).
#'
#'@param x Numeric vector.
#'@param group Factor vector of length(x).
#'@param n_sd Cutoff for outliers in E being mean(group)+-n_sd*sd(group) where group values are calculated without the outlier candidate.
#'@param method Different variants of the result value. See details.
#'
#'@return
#'Depending on method. See details.
#'
#'@examples
#'set.seed(0)
#'x <- runif(10)
#'x <- 2
#'group <- gl(2,5)
#'CheckForOutliers(x, group, method="dist")
#'CheckForOutliers(x, group, method="logical")
#'CheckForOutliers(x, group, method="idx")
#'graphics::par(mfrow=c(1,2))
#'bg <- c(3,2)[1+CheckForOutliers(x, group, method="logical")]
#'graphics::plot(x=as.numeric(group), y=x, pch=21, cex=3, bg=bg, main="n_sd=3",las=1,xlim=c(0.5,2.5))
#'bg <- c(3,2)[1+CheckForOutliers(x, group, n_sd=4, method="logical")]
#'graphics::plot(x=as.numeric(group), y=x, pch=21, cex=3, bg=bg, main="n_sd=4",las=1,xlim=c(0.5,2.5))
#'graphics::par(mfrow=c(1,1))
#'
#'# load raw data and sample description
#'utils::data(raw, package = "MetabolomicsBasics")
#'utils::data(sam, package = "MetabolomicsBasics")
#'
#'# no missing data in this matrix
#'all(is.finite(raw))
#'
#'# check for outliers (computing n-fold sd distance from group mean)
#'tmp <- apply(raw, 2, CheckForOutliers, group=sam\$GT, method="dist")
#'# plot a histogram of the observed distances
#'graphics::hist(tmp, breaks=seq(0,ceiling(max(tmp))), main="n*SD from mean", xlab="n")
#'
#'# Calculate the amount of values exceeding five-sigma and compare with a standard gaussian
#'table(tmp>5)
#'round(100*sum(tmp>5)/length(tmp),2)
#'\donttest{
#'gauss <- CheckForOutliers(x=rnorm(prod(dim(raw))), method="dist")
#'sapply(1:5, function(i) {data.frame("obs"=sum(tmp>i), "gauss"=sum(gauss>i))})
#'
#'# compare a PCA w/wo outliers
#'RestrictedPCA(dat=raw, sam=sam, use.sam=sam\$GT%in%c("Mo17","B73"), group.col="GT",
#'              fmod="GT+Batch+Order", P=1, sign.col="GT", legend.x=NULL, text.col="Batch", medsd=TRUE)
#'raw_filt <- raw
#'raw_filt[tmp>3] <- NA
#'RestrictedPCA(dat=raw_filt, sam=sam, use.sam=sam\$GT%in%c("Mo17","B73"), group.col="GT",
#'              fmod="GT+Batch+Order", P=1, sign.col="GT", legend.x=NULL, text.col="Batch", medsd=TRUE)
#'}
#'
#'@export
#'
#'@importFrom stats na.omit
#'@importFrom stats sd
#'

CheckForOutliers <- function(x=NULL, group=NULL, n_sd=3, method=c("idx","logical","dist")) {
method <- match.arg(method)
if (is.null(group)) group <- gl(1, length(x))
test <- sapply(1:length(x), function(i) {
ig <- na.omit(x[setdiff(which(group==group[i]), i)])
if (length(ig)>=2) {
return(abs(mean(ig)-x[i])/sd(ig))
} else {
return(0)
}
})
if (method=="idx") return(which(test>=n_sd))
if (method=="logical") return(test>=n_sd)
if (method=="dist") return(test)
}
```

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MetabolomicsBasics documentation built on May 2, 2019, 7:25 a.m.