# cmodes: Mode Mass Function In extremis: Statistics of Extremes

 cmodes R Documentation

## Mode Mass Function

### Description

This function computes the mode mass function.

### Usage

```cmodes(Y, thresholds = apply(Y[, -1], 2, quantile, probs =
0.95), nu = 100, ...)
```

### Arguments

 `Y` data frame from which the estimate is to be computed; first column corresponds to time and the second to the variable of interest. `thresholds` values used to threshold the data `y`; by default `threshold = quantile(y, 0.95)`. `nu` concentration parameter of beta kernel used to smooth mode mass function. `...` further arguments for `density` methods.

### Details

The scedasis functions on which the mode mass function is based are computed using the default `"nrd0"` option for bandwidth.

### Value

 `c` scedasis density estimators. `k` number of exceedances above the threshold. `w` standardized indices of exceedances. `Y` raw data.

The `plot` method depicts the smooth mode mass function along with the smooth scedasis densities.

### Author(s)

Miguel de Carvalho

### References

Rubio, R., de Carvalho, M., and Huser, R. (2018) Similarity-Based Clustering of Extreme Losses from the London Stock Exchange. Submitted.

### Examples

```data(lse)
attach(lse)
nlr <- -apply(log(lse[, -1]), 2, diff)
Y <- data.frame(DATE[-1], nlr)
T <- dim(Y)[1]
k <- floor((0.4258597) * T / (log(T)))
fit <- cmodes(Y, thresholds = as.numeric(apply(nlr, 2, sort)[T - k, ]),
kernel = "biweight", bw = 0.1 / sqrt(7), nu = 100)
plot(fit)
```

extremis documentation built on Dec. 9, 2022, 5:08 p.m.