varset                 package:ipred                 R Documentation

_S_i_m_u_l_a_t_i_o_n _M_o_d_e_l

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

     Three sets of variables are calculated: explanatory, intermediate
     and response variables.

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

     varset(N, sigma=0.1, theta=90, threshold=0, u=1:3)

_A_r_g_u_m_e_n_t_s:

       N: number of simulated observations.

   sigma: standard deviation of the error term.

   theta: angle between two u vectors.

threshold: cutpoint for classifying to 0 or 1.

       u: starting values.

_D_e_t_a_i_l_s:

     For each observation values of two explanatory variables x = (x_1,
     x_2)^{top} and of two responses y = (y_1, y_2)^{top} are
     simulated, following the formula:

             y = U*x+e = ({u_1^{top} atop u_2^{top}})*x+e

     where x is the evaluation of as standard normal random variable
     and e is generated by a normal variable with standard deviation
     'sigma'. U is a 2*2 Matrix, where 

 u_1 = ({u_{1, 1} atop u_{1, 2}}), u_2 = ({u_{2, 1} atop u_{2, 2}}), ||u_1|| = ||u_2|| = 1,

     i.e. a matrix of two normalised vectors.

_V_a_l_u_e:

     A list containing the following arguments 

explanatory: N*2 matrix of 2 explanatory variables.

intermediate: N*2 matrix of 2 intermediate variables.

response: response vectors with values 0 or 1.

_A_u_t_h_o_r(_s):

     Andrea Peters <Peters.Andrea@imbe.imed.uni-erlangen.de>

_R_e_f_e_r_e_n_c_e_s:

     David J. Hand, Hua Gui Li, Niall M. Adams (2001), Supervised
     classification with structured class definitions. _Computational
     Statistics & Data Analysis_ *36*, 209-225.

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

     theta90 <- varset(N = 1000, sigma = 0.1, theta = 90, threshold = 0)
     theta0 <- varset(N = 1000, sigma = 0.1, theta = 0, threshold = 0)
     par(mfrow = c(1, 2))
     plot(theta0$intermediate)
     plot(theta90$intermediate)

