# midquantile: Mid-distribution Functions In Qtools: Utilities for Quantiles

## Description

Compute mid-cumulative probabilities and mid-quantiles

## Usage

 ```1 2``` ```midecdf(x, na.rm = FALSE) midquantile(x, probs = 1:3/4, na.rm = FALSE) ```

## Arguments

 `x` numeric vector of observations used to estimate the mid-cumulative distribution or the mid-quantiles. `probs` numeric vector of probabilities with values in [0,1]. `na.rm` logical value indicating whether NA values should be stripped before the computation proceeds.

## Value

An object of class `class` `midecdf` or `midquantile` with mid-cumulative probabilities and mid-quantiles. For `midecdf`, this is a list that contains:

 `x` unique values of the vector `x` at which mid-cumulative probabilities are calculated. `y` estimated mid-cumulative probabilities. `fn` interpolating function of the points `(x,y)`. `data` input values.

For `midquantile`, this is a list that contains:

 `x` probabilities `probs` at which mid-quantiles are calculated. `y` estimated mid-cumulative probabilities. `fn` interpolating function of the points `(x,y)`. `data` input values.

Marco Geraci

## References

Ma Y., Genton M., and Parzen E. Asymptotic properties of sample quantiles of discrete distributions. Annals of the Institute of Statistical Mathematics 2011;63(2):227-243

Parzen E. Quantile probability and statistical data modeling. Statistical Science 2004;19(4):652-62.

## See Also

`confint.midquantile`, `plot.midquantile`

## Examples

 ```1 2``` ```x <- rpois(100, lambda = 3) midquantile(x) ```

Qtools documentation built on May 29, 2017, 12:40 p.m.