View source: R/optCluster-Functions.R

valPlot | R Documentation |

`valPlot`

displays a plot of the scores for each selected validation measure.

valPlot(x, measures = measureNames(x), legend = TRUE, legendLoc = "topright", main = NULL, pch = NULL, type = "b", ask = prod(par("mfcol")) < length(measures) && dev.interactive(), ...)

`x` |
An object of class |

`measures` |
Character vector of the names of the validation measures to plot. Any number of choices is allowed. |

`legend` |
If TRUE, provides a legend. |

`legendLoc` |
Character string specifying the location of the legend. |

`main` |
Character string specifying the title of graph. |

`pch` |
Specifies the plotting characters to use. |

`type` |
A character string specifying the type of plot. |

`ask` |
If TRUE, the user is prompted before each plot. |

`...` |
Additional plotting parameters. |

The the biological homogeneity index (BHI), biological stability index (BSI), Dunn index, and silhouette width measures should all be maximized.

The average proportion of non-overlap (APN), average distance (AD), average distance between means (ADM), figure of merit (FOM), and connectivity measures should all be minimized.

`clValid-class`

, `optCluster-class`

## This example may take a few minutes to compute ## Obtain Dataset data(arabid) ## Normalize Data with Respect to Library Size obj <- t(t(arabid)/colSums(arabid)) ## Analysis of Normalized Data using Internal and Stability Validation Measures norm1 <- optCluster(obj, 2:4, clMethods = "all") ## Plots of Internal and Stability Validation Measures par(mfrow = c(4,2)) valPlot(norm1) ## Plots of Internal Validation Measures in a Single Figure par(mfrow = c(2,2)) valPlot(norm1, measure = c("Dunn", "Silhouette", "Connectivity"), legend = FALSE) plot(0, type="n", axes=FALSE, xlab = "", ylab = "") legend("center", methodNames(norm1), col=1:9, lty=1:9, pch=paste(c(1:9)), cex=0.8)

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