ceuclide

This function takes a dataframe and a factor in input, and returns a matrix with the Euclidean distances about it.

It provides functions that calculate Mahalanobis distance, Euclidean distance, Manhattan distance, Chebyshev distance, Hamming distance, Canberra distance, Minkowski distance, Cosine distance, Bhattacharyya distance, Jaccard distance, Hellinger distance, Bray-Curtis distance, Sorensen-Dice distance between each pair of species in a list of data frames. These metrics are fundamental in various fields, such as cluster analysis, classification, and other applications of machine learning and data mining, where assessing similarity or dissimilarity between data is crucial. The package is designed to be flexible and easily integrated into data analysis workflows, providing reliable tools for evaluating distances in multidimensional contexts.

Flavio Gioia

cmahalanobis

Calculate Distance Measures for a Given List of Data Frames with
Factors

ceuclide function

<dl><dt>dataset</dt>
<dd>A dataframe.</dd>
<dt>formula</dt>
<dd>The factor which you want to calculate the Euclidean distances matrix.</dd>
<dt>plot</dt>
<dd>If TRUE, shows a plot of the Euclidean distances matrix.</dd>
<dt>plot_title</dt>
<dd>The title of the plot.</dd></dl>

Arguments

Calculate the Euclidean distance of a factor in a dataframe. — ceuclide

<dl>

<dt>dataset</dt>
<dd>A dataframe.</dd>


<dt>formula</dt>
<dd>The factor which you want to calculate the Euclidean distances matrix.</dd>


<dt>plot</dt>
<dd>If TRUE, shows a plot of the Euclidean distances matrix.</dd>


<dt>plot_title</dt>
<dd>The title of the plot.</dd>

</dl>

Last chance! 50% off unlimited learning

ceuclide: Calculate the Euclidean distance of a factor in a dataframe.

Description

Usage

Value

Arguments

Details

Examples