ionosphere: The Ionosphere dataset

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

Description

The Ionosphere dataset from the UCI Machine Learning Repository

Usage

data(ionosphere)

Format

A data frame with 351 observations on the following 33 variables.

Details

The original dataset contains 34 predictors, but we have eliminated the two first features, because the first feature had the same value in one of the classes and the second feature assumes the value 0 in all observations.

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

#---Outlier detection in  ionosphere class-1 using the Mahalanobis distiance----
data(ionosphere)
mahaout(ionosphere,1)

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 
Ouliers given by the boxplot of the  Mahalanobis distance
     115       27      272 
12.96700 12.68787 11.86717 

$outme
      115        27       272       143       275        70       231       313 
12.966997 12.687866 11.867165 11.460713 11.431267 11.169341 10.929060 10.898057 
      118       290       200       257       110       172       294        38 
10.806016 10.727117 10.704994 10.378539 10.293997 10.064789 10.057415  9.992291 
      164        41       351       307       273       148       266       144 
 9.863106  9.862989  9.825559  9.396631  9.385295  9.208611  9.064129  9.039424 
      348        39       263       295       298       281       249       239 
 8.886604  8.647847  8.601908  8.575358  8.528415  8.336114  8.242017  8.223519 
       66       304       137        29        23       283        25        50 
 8.153396  8.086737  8.044677  8.043519  8.027346  8.015211  7.925383  7.791989 
        9        32        62       282       305        94       311       165 
 7.686399  7.518011  7.483942  7.301468  7.246875  7.214722  7.185513  7.150828 
      206        42       258       109       265       155       213       271 
 7.141740  7.084486  7.001747  6.925393  6.701354  6.701152  6.691678  6.666784 
       43       175       134        15        90       299       214       173 
 6.518256  6.430749  6.378421  6.182562  6.142469  6.103678  6.102048  6.051873 
       26       327       270       322        14       103       181       321 
 6.036902  6.015007  6.012037  5.991317  5.918798  5.834993  5.773905  5.761145 
      344        35         8       309       116       208        68       212 
 5.754443  5.736882  5.639997  5.548631  5.535852  5.468769  5.437791  5.410863 
      161        46        20       168        31       264        11       243 
 5.354982  5.326981  5.320164  5.309286  5.252225  5.246686  5.197210  5.174492 
      284       126        69       296       236       145       342       220 
 5.156558  5.067577  5.028895  4.947865  4.928340  4.828512  4.788916  4.743381 
       57       122       248       277       178       191       104        59 
 4.732342  4.658915  4.657948  4.582986  4.582187  4.569154  4.541794  4.526771 
      250       146       135       341         4        54       198        80 
 4.501076  4.359307  4.353891  4.284854  4.268186  4.215637  4.171406  4.104016 
       18       180       176       223        24        86        74       184 
 4.027183  4.014645  4.013309  3.993233  3.987827  3.982318  3.977467  3.959924 
       76       340       177        53       171       268        52       167 
 3.940995  3.939413  3.904828  3.892498  3.862755  3.755569  3.692698  3.686792 
       22       319        96       169        55       253       192       345 
 3.680766  3.662957  3.658775  3.646951  3.645496  3.605612  3.596143  3.593559 
      350        71       226       314       210        82        77         6 
 3.589833  3.584166  3.574713  3.527686  3.490908  3.489290  3.481833  3.470824 
       67        72       133       285         2       136       235       219 
 3.460234  3.453515  3.436700  3.396584  3.390581  3.389826  3.364244  3.363516 
       92       179       269       182        10       247        47       163 
 3.321544  3.310471  3.303155  3.283902  3.275855  3.242564  3.232642  3.221011 
       56       130       233       228       185       123       102       234 
 3.197108  3.184342  3.176326  3.132374  3.105918  3.101743  3.101382  3.090424 
      312       131       190        63       347       240       106       120 
 3.088084  3.054322  3.048503  3.024330  2.991015  2.959063  2.939611  2.937155 
      189       188        13       334        48       329        40       153 
 2.914913  2.895785  2.889628  2.831784  2.762144  2.749948  2.729145  2.690578 
      343        89       128        44       230        49       251       300 
 2.677576  2.653054  2.642427  2.637915  2.615845  2.599082  2.574345  2.382535 
      129       217       100         7        51         1       315       203 
 2.346529  2.322357  2.284214  2.280140  2.244116  2.238395  2.212948  2.167268 
      244       141       149       337       156       256       232       317 
 2.086925  2.046646  2.041727  1.974911  1.966278  1.939506  1.937758  1.918688 
      238       150        30       288        12       280       259        95 
 1.845629  1.827207  1.817729  1.796300  1.775276  1.761293  1.741101  1.698380 
      241       142       229        37       199        88       218        17 
 1.692984  1.681683  1.678441  1.672192  1.646785  1.644683  1.490526  1.444078 
      222 
 1.382434 

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