# plotBIC: Plot Bayesian Information Criterion as a Function of Number... In Ckmeans.1d.dp: Optimal, Fast, and Reproducible Univariate Clustering

## 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

 ```1 2 3 4 5 6 7 8``` ```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 `Ckmeans.1d.dp` returned by `Ckmeans.1d.dp`. `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 `plot`. `main` a character string. The title for the plot. `sub` a character string. The subtitle for the plot. `...` arguments passed to `plot` function in package graphics.

## 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 \insertCitesong2020wucCkmeans.1d.dp.

## Value

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

Joe Song

\insertAllCited

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```# 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) ```

### Example output  ```
```

Ckmeans.1d.dp documentation built on July 22, 2020, 5:09 p.m.