vehicle                package:dprep                R Documentation

_T_h_e _V_e_h_i_c_l_e _d_a_t_a_s_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     This is the vehicle dataset from the UCI Machine Learning
     Repository

_U_s_a_g_e:

     data(vehicle)

_F_o_r_m_a_t:

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

     _V_1 Compactness

     _V_2 Circularity

     _V_3 Distance Circularity

     _V_4 Radius ratio

     _V_5 pr.axis aspect ratio

     _V_6 max.length aspect ratio

     _V_7 scatter ratio

     _V_8 elongatedness

     _V_9 pr.axis rectangularity

     _V_1_0 max.length rectangularity

     _V_1_1 scaled variance along major axis

     _V_1_2 scaled variance along minor axis

     _V_1_3 scaled radius of gyration

     _V_1_4 skewness about major axis

     _V_1_5 skewness about minor axis

     _V_1_6 kurtosis about minor axis

     _V_1_7 kurtosis about major axis

     _V_1_8 hollows ratio

     _V_1_9 Type of vehicle: a double decker bus, Cheverolet van, Saab
          9000 and an Opel Manta 400.

_S_o_u_r_c_e:

     The UCI Machine Learning Database Repository at:

        *  <URL: ftp://ftp.ics.uci.edu/pub/machine-learning-databases>

        *  <URL: http://www.ics.uci.edu/~mlearn/MLRepository.html>

_E_x_a_m_p_l_e_s:

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

