plotBIC {Ckmeans.1d.dp} | R Documentation |

## Plot Bayesian Information Criterion as a Function of Number of Clusters

### Description

Plot Bayesian information criterion (BIC) as a function of the number of clusters obtained from optimal univariate clustering results returned from `Ckmeans.1d.dp`

. The BIC normalized by sample size (BIC/n) is shown.

### Usage

```
plotBIC(
ck, xlab="Number of clusters k",
ylab = "BIC/n", type="b",
sub=paste("n =", length(ck$cluster)),
main=paste("Bayesian information criterion",
"(normalized by sample size)", sep="\n"),
...
)
```

### Arguments

`ck` |
an object of class |

`xlab` |
a character string. The x-axis label for the plot. |

`ylab` |
a character string. The x-axis label for the plot. |

`type` |
the type of plot to be drawn. See |

`main` |
a character string. The title for the plot. |

`sub` |
a character string. The subtitle for the plot. |

`...` |
arguments passed to |

### Details

The function visualizes the input data as sticks whose heights are the weights. It uses different colors to indicate optimal `k`-means clusters. The method to calcualte BIC based on Gaussian mixture models estimated on a univariate clustering is described in (Song and Zhong 2020).

### Value

An object of class "`Ckmeans.1d.dp`

" defined in `Ckmeans.1d.dp`

.

### Author(s)

Joe Song

### References

Song M, Zhong H (2020).
“Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers.”
*Bioinformatics*, **36**(20), 5027–5036.
doi:10.1093/bioinformatics/btaa613.

### Examples

```
# Example: clustering data generated from a Gaussian mixture
# model of two components
x <- rnorm(50, mean=-1, sd=0.3)
x <- append(x, rnorm(50, mean=1, sd=0.3) )
res <- Ckmeans.1d.dp(x)
plotBIC(res)
y <- (rnorm(length(x)))^2
res <- Ckmeans.1d.dp(x, y=y)
plotBIC(res)
```

*Ckmeans.1d.dp*version 4.3.5 Index]