testLmFisher           package:rotRPackage           R Documentation

_F_i_s_h_e_r _t_e_s_t _f_o_r _a _l_i_n_e_a_r _m_o_d_e_l.

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

     This ROT function, called from a Test C++ object, is given two
     samples, a   scalar and a parameter vector. It predicts the values
     corresponding to the   explanatory variables through the linear
     model, then computes the Fisher     statistic. It is tested
     against the scalar, then the function returns the    result of the
     test and the Fisher value.

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

     testLmFisher(x, beta, y, testLevel = 0.95)

_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 containng the linear model parameters.

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

testLevel: the test level. (scalar in [0:1])

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

     As it is not asked in LinearModel.getPredict(), no prediction
     interval  is returned; it is up to the user to be careful about
     that. It is also to   noted that the sample is not assumed to
     contain the '1's corresponding to   the intercept parameter.

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

     A list is returned, containing two scalars ,                   

testResult: A scalar simulating a boolean (easier for Rserve)

valueFisher: A scalar.

_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 + matrix(rnorm(10, 0, 0.05), 10, 1)
     LM <- computeLinearModel(x, y)
     testLmFisher(x, LM$parameterEstimate, y) 

