summarization {ECoL}

Post processing complexity measures

Description

Post-processing alternatives to deal with multiples values. This method is used by the complexity measures to summarize the obtained values.

Usage

summarization(measure, summary = c("mean", "sd"), multiple = TRUE, ...)

Arguments

measure

A list with the complexity measures values.

summary

The functions to post processing the data. See the details to more information. Default: c("mean", "sd")

multiple

A logical value defining if the measure should return multiple values. (Default: TRUE)

...

Extra values used to the functions of summarization.

Details

The post processing functions are used to summarize the complexity measures. They are organized into three groups: return, descriptive statistic and distribution. Currently, the hypothesis testing post processing are not supported.

In practice, there are no difference among the types, so that more than one type and functions can be combined. Usually, these function are used to summarize a set of values for each complexity measures. For instance, a measure computed for each attribute can be summarized using the "mean" and/or "sd".

In addition to the native functions available in R, the following functions can be used:

"histogram"

Computes a histogram of the given data value. The extra parameters 'bins' can be used to define the number of values to be returned. The parameters 'max' and 'min' are used to define the range of the data. The default value for these parameters are respectively 10, min(x) and max(x).

"kurtosis"

See kurtosis

"max"

See max

"mean"

See mean

"median"

See median

"min"

See min

"quantiles"

See quantile

"sd"

See sd

"skewness"

See skewness

"var"

See var

"return"

Returns the original value(s) of the complexity measure.

These functions are not restrictive, thus another functions can be applied as post-processing summarization function.

Value

A list with the post-processed complexity measures.

References

Albert Orriols-Puig, Nuria Macia and Tin K Ho. (2010). Documentation for the data complexity library in C++. Technical Report. La Salle - Universitat Ramon Llull.

Examples

summarization(runif(15))
summarization(runif(15), c("min", "max"))
summarization(runif(15), c("quantiles", "skewness"))