sonar: The Sonar dataset

In dprep: Data Pre-Processing and Visualization Functions for Classification

Description

This is the sonar dataset. It containis information on 208 objects and 60 attributes. The objects are classified in two classes: "rock" and "mine".

Usage

data(sonar)

Format

A data frame with 208 observations on 61 variables. The first 60 represent the energy within a particular frequency band,integrated over a certain period of time. The last column contains the class labels. There are two classes 0 if the object is a rock, and 1 if the object is a mine (metal cylinder). The range value of each attribute varies from 0.0 to 1.0.

Source

The UCI Machine Learning Database Repository at:

• ftp://ftp.ics.uci.edu/pub/machine-learning-databases

• http://www.ics.uci.edu/~mlearn/MLRepository.html

Examples

## Not run: #Robust detection of outliers in sonar-class1 using MVE----
data(sonar)
robout(sonar,1,"mve",rep=10)

## End(Not run)

Example output

Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 

Top outliers by frequency

103 128 139 140 146 149 
  6   7   7   6   6   6 

Top outliers by outlyngness measure
      99      149      128      139      148      146 
33.06351 26.87076 26.47680 24.92685 23.90984 23.10563 

$outme
       99       149       128       139       148       146       140       143 
33.063511 26.870756 26.476799 24.926847 23.909839 23.105628 20.755540 18.606843 
      144       135       147       131       136       134       101       102 
18.254264 18.095574 17.953736 17.815232 17.802473 17.620443 17.594833 17.441250 
      132       103       133       141       187       137       142       145 
16.440114 16.301061 16.105987 15.797498 15.723144 15.666297 14.849573 14.783253 
      100        98       130       174       166       105       112       129 
14.640140 14.370537 14.329685 13.968622 13.559110 13.537129 13.533980 13.401487 
      116       104       173       106       165       185       109       160 
13.241819 13.124170 13.080079 13.041093 12.746089 12.281457 11.731715 11.685235 
      182       181       117       151       172       150       177       152 
11.511233 11.410205 10.954186 10.878694 10.715889 10.556828 10.263754 10.108415 
      111       127       206       125       208       113       153       123 
10.094711 10.036601  9.936866  9.778398  9.770049  9.697858  9.662155  9.525236 
      167       175       108       180       194       126       168       189 
 9.313085  9.232747  9.224661  9.114022  9.036489  8.994845  8.970032  8.870346 
      115       138       118       120       193       159       155       164 
 8.834759  8.813077  8.739636  8.645626  8.643730  8.585952  8.550553  8.472721 
      184       205       110       186       121       183       171       156 
 8.434763  8.424911  8.382099  8.222683  8.157698  8.140296  8.113935  8.113540 
      107       114       179       178       124       203       170       169 
 8.091400  7.970021  7.969176  7.930951  7.899434  7.686671  7.609959  7.486732 
      176       195       119       154       207       200       188       157 
 7.381848  7.358423  7.328768  7.232404  7.183045  7.131429  7.120687  7.097307 
      204       190       162       122       201       192       191       163 
 7.070371  6.881574  6.857391  6.832211  6.811547  6.772145  6.753914  6.692195 
      158       197       161       202       198       196       199 
 6.575788  6.338874  6.273562  6.256385  6.004147  5.485675  4.922346 

dprep documentation built on May 29, 2017, 11:01 a.m.