midquantile {Qtools} | R Documentation |
Mid-distribution Functions
Description
Compute mid-cumulative probabilities and mid-quantiles
Usage
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 |
y |
estimated mid-cumulative probabilities. |
fn |
interpolating function of the points |
data |
input values. |
For midquantile
, this is a list that contains:
x |
probabilities |
y |
estimated mid-cumulative probabilities. |
fn |
interpolating function of the points |
data |
input values. |
Author(s)
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
x <- rpois(100, lambda = 3)
midquantile(x)