vehicle: The Vehicle dataset

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

Description

This is the Vehicle dataset from the UCI Machine Learning Repository

Usage

data(vehicle)

Format

A data frame with 846 observations on the following 19 variables.

V1

Compactness

V2

Circularity

V3

Distance Circularity

V4

Radius ratio

V5

pr.axis aspect ratio

V6

max.length aspect ratio

V7

scatter ratio

V8

elongatedness

V9

pr.axis rectangularity

V10

max.length rectangularity

V11

scaled variance along major axis

V12

scaled variance along minor axis

V13

scaled radius of gyration

V14

skewness about major axis

V15

skewness about minor axis

V16

kurtosis about minor axis

V17

kurtosis about major axis

V18

hollows ratio

V19

Type of vehicle: a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400.

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

#----feature selection using sequential floating selection with LDA----
data(vehicle)
mahaout(vehicle,nclass=3)

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
     232      197 
7.228811 5.892048 

$outme
     232      197      250      835      298      184      352       78 
7.228811 5.892048 5.780524 5.650073 5.635254 5.590947 5.533337 5.507308 
     615      514      762      555      124       39      149      563 
5.469245 5.401379 5.321845 5.319981 5.288914 5.269041 5.246247 5.237760 
     379      770      523      566      689       28      624      810 
5.222813 5.218246 5.203683 5.178422 5.143156 5.110028 5.101182 5.086030 
     164      784       12      606      361      423      845      507 
5.080511 5.074089 5.055984 5.051949 5.029854 5.008166 4.988641 4.979901 
      30      311      408      821      368      637      290      347 
4.966557 4.936515 4.932848 4.924732 4.909033 4.899641 4.898513 4.896126 
     318      544       97      663      767       32      600      842 
4.847787 4.846917 4.843276 4.840597 4.832115 4.805525 4.802002 4.799497 
     420      185      440      604      741      750      343      751 
4.794734 4.690085 4.671861 4.636428 4.617197 4.579775 4.567464 4.539159 
     560      121      181      141      844      411      626      286 
4.536628 4.522812 4.521111 4.520120 4.506044 4.503690 4.478534 4.455012 
     139       52      168       44      476       51      798      655 
4.450473 4.446536 4.445469 4.444797 4.442964 4.418553 4.414348 4.409117 
     272      807      662       45      787      722      167      690 
4.386681 4.374100 4.350401 4.345862 4.343317 4.335083 4.333419 4.332849 
     395      230      284      652      757      202      518      594 
4.317495 4.308503 4.306632 4.296353 4.296134 4.275871 4.270042 4.266172 
     299      431      132      455      401      621      131      537 
4.221740 4.202579 4.201783 4.201447 4.196171 4.193605 4.193201 4.170997 
     256      506       10      697      691      567      675      159 
4.168933 4.161245 4.160114 4.157831 4.153921 4.151865 4.150830 4.117306 
     572      244      265      834      533       93      527       50 
4.093235 4.074921 4.072391 4.066347 4.045503 4.044951 4.037252 4.036718 
     521      217      133      492      268      227      577      433 
4.034195 4.027706 4.016719 4.006185 3.992573 3.992320 3.974308 3.973788 
     571       27      377      325      818      460      410      651 
3.970625 3.966499 3.962727 3.951261 3.950919 3.950820 3.950424 3.949589 
     833      558      623      808      378      744      758      435 
3.948157 3.940797 3.940184 3.935145 3.931887 3.927204 3.919223 3.911941 
     363      631      531      765      583      737      321      777 
3.911051 3.877399 3.873053 3.870191 3.852771 3.850507 3.835323 3.833336 
      19      789      781      248      261      754      151      491 
3.807427 3.784610 3.772515 3.770335 3.769881 3.766464 3.764335 3.761102 
     668       25      481      362      358      301      649      550 
3.756034 3.751939 3.748329 3.733254 3.732559 3.730337 3.728685 3.722147 
     720      429      307      262      257      660      779      193 
3.721245 3.706652 3.704712 3.694738 3.653024 3.651895 3.644576 3.636169 
      91      324      625      717      553      441      225      229 
3.633255 3.627629 3.620431 3.620018 3.618620 3.613280 3.598529 3.591056 
     643      488      695      526      702        3      712      195 
3.584558 3.577987 3.572656 3.561724 3.533661 3.530437 3.521380 3.504015 
     154      336      366      279      259      106      543      447 
3.502435 3.502224 3.495204 3.493339 3.486230 3.482701 3.479570 3.478627 
     504      118      584      838      163      790      469      576 
3.475645 3.453544 3.408275 3.397548 3.396147 3.371900 3.325411 3.320660 
     727      568      234      387       77      820      648      713 
3.299851 3.251366 3.216267 3.211738 3.205496 3.203770 3.201519 3.170909 
     519      355      693      706       57      330      664      714 
3.126901 3.108872 3.055243 3.050834 2.957663 2.910735 2.908523 2.905870 
     650 
2.820568 

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