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.

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

`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 |

`FUN` |
specifies the function used to measure separation between groups |

`values` |
if |

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

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]))
}
``` |

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