M5data: Mixture M5 Data.
In fsdaR: Robust Data Analysis Through Monitoring and Dynamic Visualization
M5data R Documentation
Mixture M5 Data.
Description
A bivariate data set obtained from three normal bivariate distributions with different scales and
proportions 1:2:2. One of the components is strongly overlapping with another one. A 10
noise is added uniformly distributed in a rectangle containing the three normal components and not
strongly overlapping with the three mixture components. A precise description of the M5 data set
can be found in Garcia-Escudero et al. (2008).
Usage
data(M5data)
Format
A data frame with 2000 rows and 3 variables
The first two columns are the two variables. The last column is the true classification vector where symbol "0" stands for the contaminating data points.
Source
Garcia-Escudero, L.A., Gordaliza, A., Matran, C. and Mayo-Iscar, A. (2008). A General Trimming Approach to Robust Cluster Analysis,
Annals of Statistics, Vol.36, 1324-1345. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/07-AOS515")}.
fsdaR documentation built on March 31, 2023, 8:18 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| M5data | R Documentation |
Mixture M5 Data.
Description
A bivariate data set obtained from three normal bivariate distributions with different scales and proportions 1:2:2. One of the components is strongly overlapping with another one. A 10 noise is added uniformly distributed in a rectangle containing the three normal components and not strongly overlapping with the three mixture components. A precise description of the M5 data set can be found in Garcia-Escudero et al. (2008).
Usage
data(M5data)
Format
A data frame with 2000 rows and 3 variables The first two columns are the two variables. The last column is the true classification vector where symbol "0" stands for the contaminating data points.
Source
Garcia-Escudero, L.A., Gordaliza, A., Matran, C. and Mayo-Iscar, A. (2008). A General Trimming Approach to Robust Cluster Analysis, Annals of Statistics, Vol.36, 1324-1345. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/07-AOS515")}.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.