Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

fish

Fish Catch Data Set


Description

The Fish Catch data set contains measurements on 159 fish caught in the lake Laengelmavesi, Finland.

Usage

data(fish)

Format

A data frame with 159 observations on the following 7 variables.

Weight

Weight of the fish (in grams)

Length1

Length from the nose to the beginning of the tail (in cm)

Length2

Length from the nose to the notch of the tail (in cm)

Length3

Length from the nose to the end of the tail (in cm)

Height

Maximal height as % of Length3

Width

Maximal width as % of Length3

Species

Species

Details

The Fish Catch data set contains measurements on 159 fish caught in the lake Laengelmavesi, Finland. For the 159 fishes of 7 species the weight, length, height, and width were measured. Three different length measurements are recorded: from the nose of the fish to the beginning of its tail, from the nose to the notch of its tail and from the nose to the end of its tail. The height and width are calculated as percentages of the third length variable. This results in 6 observed variables, Weight, Length1, Length2, Length3, Height, Width. Observation 14 has a missing value in variable Weight, therefore this observation is usually excluded from the analysis. The last variable, Species, represents the grouping structure: the 7 species are 1=Bream, 2=Whitewish, 3=Roach, 4=Parkki, 5=Smelt, 6=Pike, 7=Perch. This data set was also analyzed in the context of robust Linear Discriminant Analysis by Todorov (2007), Todorov and Pires (2007).

Source

Journal of Statistical Education, Fish Catch Data Set, [http://www.amstat.org/publications/jse/datasets/fishcatch.txt] accessed August, 2006.

References

Todorov, V. (2007 Robust selection of variables in linear discriminant analysis, Statistical Methods and Applications, 15, 395–407, doi:10.1007/s10260-006-0032-6.

Todorov, V. and Pires, A.M. (2007) Comparative performance of several robust linear discriminant analysis methods, REVSTAT Statistical Journal, 5, 63–83.

Examples

data(fish)

    # remove observation #14 containing missing value
    fish <- fish[-14,]

    # The height and width are calculated as percentages 
    #   of the third length variable
    fish[,5] <- fish[,5]*fish[,4]/100
    fish[,6] <- fish[,6]*fish[,4]/100
 
    # plot a matrix of scatterplots
    pairs(fish[1:6],
          main="Fish Catch Data",
          pch=21,
          bg=c("red", "green3", "blue", "yellow", "magenta", "violet", 
          "turquoise")[unclass(fish$Species)])

rrcov

Scalable Robust Estimators with High Breakdown Point

v1.5-5
GPL (>= 2)
Authors
Valentin Todorov [aut, cre] (<https://orcid.org/0000-0003-4215-0245>)
Initial release
2020-07-31

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.