# chi v0.1

Monthly downloads

## The Chi Distribution

Light weight implementation of the standard distribution
functions for the chi distribution, wrapping those for the chi-squared
distribution in the stats package.

## Readme

**chi**

**chi** implements the `(d/p/q/r)`

statistics functions for the chi distribution in R. It is ideal for using in other packages since it is lightweight and leverages the `(d/p/q/r)chisq()`

line of functions maintained by CRAN.

### Getting **chi**

There are two ways to get **chi**. For the CRAN version, use

```
install.packages("chi")
```

For the development version, use

```
# install.packages("devtools")
devtools::install_github("dkahle/chi")
```

### The `(d/p/q/r)chi()`

functions

The defining property of the chi distribution is that it is the square root of a chi random variable.

The PDF (the *f(x)*) can be evaluated with the `dchi()`

function:

```
library(chi)
library(ggplot2); theme_set(theme_bw())
x <- seq(0, 6, .01)
qplot(x, dchi(x, 7), geom = "line")
```

The CDF can be evaluated with the `pchi()`

function:

```
f <- function(x) dchi(x, 7)
q <- 2
integrate(f, 0, q)
# 0.2202226 with absolute error < 2.4e-15
(p <- pchi(q, 7))
# [1] 0.2202226
```

The quantile function can be evaluated with `qchi()`

:

```
qchi(p, 7) # = q
# [1] 2
```

And random number generation can be performed with `rchi()`

:

```
set.seed(1)
rchi(5, 7)
# [1] 2.006520 3.389800 3.349233 2.742686 3.907928
```

`rchi()`

can be used to obtain a Monte Carlo estimate of the probability given by `pchi()`

above:

```
samples <- rchi(1e5, 7)
mean(samples <= q)
# [1] 0.22172
```

Moreover, we can check the consistency and correctness of the implementation with

```
qplot(samples, geom = "density") +
stat_function(fun = f, color = "red")
```

### Related packages

The **Runuran** package also implements a sampler from the chi distribution, using the function `Runuran::urchi()`

. (It also provides `udchi()`

, but not `upchi()`

or `uqchi()`

.) Here's a comparison of how fast the samplers are, with the punch line being that **Runuran** is about 3x faster for larger numbers of samples, whereas **chi** is much faster for smaller samples (e.g. 100 or 1000).

```
library(Runuran)
library(microbenchmark)
# chi::rchi() is much faster for small datasets
microbenchmark(
urchi(1e3, 5),
rchi(1e3, 5)
)
# Unit: microseconds
# expr min lq mean median uq max neval
# urchi(1000, 5) 784.582 808.7745 911.0466 838.4780 929.167 1859.345 100
# rchi(1000, 5) 109.266 125.1120 143.1345 131.0915 145.558 333.781 100
# cld
# b
# a
# Runuran::urchi is ~3x faster for larger datasets
microbenchmark(
urchi(1e5, 5),
rchi(1e5, 5)
)
# Unit: milliseconds
# expr min lq mean median uq
# urchi(1e+05, 5) 4.077995 4.317686 4.765376 4.512885 4.954472
# rchi(1e+05, 5) 11.912304 12.291699 13.001405 12.669712 13.460673
# max neval cld
# 6.763741 100 a
# 15.766955 100 b
```

## Functions in chi

Name | Description | |

chi | The Chi Distribution | |

invchi | The Inverse Chi Distribution | |

No Results! |

## Last month downloads

## Details

Type | Package |

URL | https://github.com/dkahle/chi |

BugReports | https://github.com/dkahle/chi/issues |

License | GPL-2 |

RoxygenNote | 6.0.1 |

NeedsCompilation | no |

Packaged | 2017-05-07 03:05:30 UTC; david_kahle |

Repository | CRAN |

Date/Publication | 2017-05-07 05:22:54 UTC |

Contributors |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/chi)](http://www.rdocumentation.org/packages/chi)
```