# CheckForOutliers. `CheckForOutliers` will evaluate a numeric vector and check if outliers within groups based on group $mean +- n x sd$. ```r CheckForOutliers( x = NULL, group = NULL, n_sd = 3, method = c("idx", "logical", "dist") ) ``` ## Arguments - `x`: Numeric vector. - `group`: Factor vector of length(x). - `n_sd`: Cutoff for outliers in E being mean(group)+-n_sd*sd(group) where group values are calculated without the outlier candidate. - `method`: Different variants of the result value. See details. ## Returns Depending on the selected method. See details. ## 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 `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). ## Examples ```r set.seed(0) x <- runif(10) x[1] <- 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 raw <- MetabolomicsBasics::raw sam <- MetabolomicsBasics::sam # 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$GT, method = "dist") # plot a histogram of the observed distances graphics::hist(tmp, breaks = seq(0, ceiling(max(tmp))), main = "n*SD from mean", xlab = "n") # Calculate the amount of values exceeding five-sigma and compare with a standard gaussian table(tmp > 5) round(100 * sum(tmp > 5) / length(tmp), 2) gauss <- CheckForOutliers(x = rnorm(prod(dim(raw))), method = "dist") sapply(1:5, function(i) { data.frame("obs" = sum(tmp > i), "gauss" = sum(gauss > i)) }) # compare a PCA w/wo outliers RestrictedPCA( dat = raw, sam = sam, use.sam = sam$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 ) ```

CheckForOutliers function