# orderFeatures: Order features, based on their ability to discriminate In hddplot: Use Known Groups in High-Dimensional Data to Derive Scores for Plots

## Description

For each row of `data`, an F or (potentially) other statistic is calculated, using the function `FUN`, that measures the extent to which this variable separates the data into groups. This statistic is then used to order the rows.

## Usage

 ```1 2``` ``` orderFeatures(x, cl, subset = NULL, FUN = aovFbyrow, values = FALSE) ```

## Arguments

 `x` Matrix; rows are features, and columns are observations ('samples') `cl` Factor that classifies columns into groups `subset` allows specification of a subset of the columns of `data` `FUN` specifies the function used to measure separation between groups `values` if `TRUE`, F-values as well as the ordering are returned

## Value

Either (`values=FALSE`) a vector that orders the rows, or (`values=TRUE`)

 `ord` a vector that orders the rows `stat` ordered values of the statistic

John Maindonald

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```mat <- matrix(rnorm(1000), ncol=20) cl <- factor(rep(1:3, c(7,9,4))) ord <- orderFeatures(mat, cl) ## The function is currently defined as function(x, cl, subset=NULL, FUN=aovFbyrow, values=FALSE){ if(dim(x)[2]!=length(cl))stop(paste("Dimension 2 of x is", dim(x)[2], "differs from the length of cl (=", length(cl))) ## Ensure that cl is a factor & has no redundant levels if(is.null(subset)) cl <- factor(cl) else cl <- factor(cl[subset]) if(is.null(subset)) stat <- FUN(x, cl) else stat <- FUN(x[, subset], cl) ord <- order(-abs(stat)) if(!values)ord else(list(ord=ord, stat=stat[ord])) } ```

hddplot documentation built on Sept. 3, 2017, 5:02 p.m.