HistDAWass (version 1.0.4)

get.MatH.stats: Method get.MatH.stats

Description

It returns statistics for each distribution contained in a MatH object.

Usage

get.MatH.stats(object, ...)
# S4 method for MatH
get.MatH.stats(object, stat = "mean", prob = 0.5)

Arguments

object

a MatH object

...

a set of other parameters

stat

(optional) a string containing the required statistic. Default='mean' - stat='mean' - for computing the mean of each histogram - stat='median' - for computing the median of each histogram - stat='min' - for computing the minimum of each histogram - stat='max' - for computing the maximum of each histogram - stat='std' - for computing the standard deviatio of each histogram - stat='skewness' - for computing the skewness of each histogram - stat='kurtosis' - for computing the kurtosis of each histogram - stat='quantile' - for computing the quantile ot level prob of each histogram

prob

(optional)a number between 0 and 1 for computing the value once choosen the 'quantile' option for stat.

Value

A list

Slots

stat

- the chosen statistic

prob

- level of probability if stat='quantile'

MAT

- a matrix of values

Examples

# NOT RUN {
get.MatH.stats(BLOOD) # the means of the distributions in BLOOD dataset
get.MatH.stats(BLOOD,stat='median') # the medians of the distributions in BLOOD dataset
get.MatH.stats(BLOOD,stat='quantile', prob=0.5) #the same as median
get.MatH.stats(BLOOD,stat='min') # minima of the distributions in BLOOD dataset
get.MatH.stats(BLOOD,stat='quantile', prob=0) #the same as min
get.MatH.stats(BLOOD,stat='max') # maxima of the distributions in BLOOD dataset
get.MatH.stats(BLOOD,stat='quantile', prob=1) #the same as max
get.MatH.stats(BLOOD,stat='std') # standard deviations of the distributions in BLOOD dataset
get.MatH.stats(BLOOD,stat='skewness') #skewness indices of the distributions in BLOOD dataset
get.MatH.stats(BLOOD,stat='kurtosis') #kurtosis indices of the distributions in BLOOD dataset
get.MatH.stats(BLOOD,stat='quantile',prob=0.05) 
#the fifth percentiles of distributions in BLOOD dataset
# }