computeResidualLm        package:rotRPackage        R Documentation

_C_o_m_p_u_t_e_s _a _l_i_n_e_a_r _m_o_d_e_l'_s _r_e_s_i_d_u_a_l_s

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

     This ROT function, called from a LinearModelFactory, is given two
     samples and a parameter vector. It is used to predict the values  
      corresponding to the explanatory variables through the linear
     model, then   compare them with the second sample. It returns the
     difference between both  samples.

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

     computeResidualLm(x, beta, y)

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

       x: A m-by-n matrix containing the explanatory variables.

    beta: A n-by-1 vector containing the linear model parameters.

       y: A n-by-1 vector containing the response variables.

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

     A m-by-1 vector is returned, containing the difference between 
     predicted and response values.

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

     Pierre-Matthieu Pair, Softia for EDF.

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

     set.seed(1)
     x <- matrix(runif(40), 10, 4)
     r <- matrix(c(1,2,3,4), 4, 1)
     y <- x %*% r 
     e <- matrix(rnorm(10, 0, 0.05), 10, 1)
     LM <- computeLinearModel(x, y + e)
     computeResidualLm(x, LM$parameterEstimate, y)

