LMCondDistribution          package:distrEx          R Documentation

_G_e_n_e_r_a_t_i_n_g _f_u_n_c_t_i_o_n _f_o_r _t_h_e _c_o_n_d_i_t_i_o_n_a_l _d_i_s_t_r_i_b_u_t_i_o_n 
_o_f _a _l_i_n_e_a_r _r_e_g_r_e_s_s_i_o_n _m_o_d_e_l.

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

     Generates an object of class '"AbscontCondDistribution"' which  is
     the conditional distribution of a linear regression model (given
     the regressor).

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

     LMCondDistribution(Error = Norm(), theta = 0, intercept = 0, scale = 1)

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

   Error: Object of class '"AbscontDistribution"':  error distribution. 

   theta: numeric vector: regression parameter. 

intercept: real number: intercept parameter. 

   scale: positive real number: scale parameter. 

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

     Object of class '"AbscontCondDistribution"'

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

     Matthias Kohl Matthias.Kohl@stamats.de

_S_e_e _A_l_s_o:

     'AbscontCondDistribution-class', 'E-methods'

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

     # normal error distribution
     (D1 <- LMCondDistribution(theta = 1)) # corresponds to Norm(cond, 1)
     plot(D1)
     r(D1)
     d(D1)
     p(D1)
     q(D1)
     param(D1)
     cond(D1)

     d(D1)(0, cond = 1)
     d(Norm(mean=1))(0)

     E(D1, cond = 1)
     E(D1, function(x){x^2}, cond = 2)
     E(Norm(mean=2), function(x){x^2})

