`scalecurve`

computes the scale curve of a given group, based on the modified band depth, at a given value p as the area of the band delimited by the [np] most central observations, where [np] is the largest integer smaller than np.

1 2 | ```
scalecurve(x,y=NULL,xlab="p",ylab="A(p)",main="Scale curve",lwd=2,
...)
``` |

`x` |
a data matrix containing the observations (samples) by rows and the variables (genes) by columns |

`y` |
an optional vector (numeric or factor) of length equal to the number of rows in |

`xlab` |
label in the x axis |

`ylab` |
label in the y axis |

`main` |
plot title |

`lwd` |
line widths for the corresponding scale curve(s) |

`...` |
graphical parameters to be passed to 'plot' |

The scale curve measures the increase in the area of the band determined by the fraction p most central curves, where p moves from 0 to 1, thus providing a measure of the sample dispersion. If the data set is represented in parallel coordinates, then the area is computed using the trapezoid formula.

`r` |
the value of the scale curve at equidistant values of p, determined by the number of observation within each class. If |

Sara Lopez-Pintado sl2929@columbia.edu and

Aurora Torrente etorrent@est-econ.uc3m.es

Lopez-Pintado, S. *et al.* (2010). Robust depth-based tools for the analysis of gene expression data. *Biostatistics*, 11 (2), 254-264.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## scale curve of a single data set
## simulated data
set.seed(0)
x <- matrix(rnorm(100),10,10)
scalecurve(x)
## real data
data(prostate)
prost.x<-prostate[,1:100]
prost.y<-prostate[,101]
scalecurve(prost.x[prost.y==0,]) ## scale curve of normal samples
scalecurve(prost.x[prost.y==1,]) ## scale curve of tumoral samples
## scalecurve of different groups
## simulated data
x <- matrix(rnorm(100),10,10)
y <- c(rep("tumoral",5),rep("normal",5))
scalecurve(x,y)
## real data
labels<-prost.y
labels[prost.y==0]<-"normal"; labels[prost.y==1]<-"tumoral"
scalecurve(prost.x,labels)
``` |

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