computeTestKolmogorovLogistic  package:rotRPackage  R Documentation

_C_o_m_p_u_t_e _t_h_e _K_o_l_m_o_g_o_r_o_v-_S_m_i_r_n_o_f_f _t_e_s_t _o_n _a _L_o_g_i_s_t_i_c _D_i_s_t_r_i_b_u_t_i_o_n _s_a_m_p_l_e.

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

     This ROT function, called from a Test C++ object, is given a
     sample, a point, the necessary distribution parameters and
     optionnaly a test level. It then returns the result of a K-S test
     against the null hypothesis that the sample has un underlying
     Logistic distribution of the given parameters and returns a list
     containing the result and test p-value.

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

     computeTestKolmogorovLogistic(numericalSample, alpha, beta, testLevel = 0.95, estimatedParameters)

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

numericalSample: the sample to be tested (numeric vector)

   alpha: The Logistic distribution alphaParameter.

    beta: The Logistic distribution betaParameter.

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

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

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

     A list is returned, containing : 

testResult: The result. 1 means H0 is not rejected. (scalar)

threshold: The threshold applied to the p-value when deciding the
          outcome of the test.

  pValue: The test p-value. (scalar)

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

     Pierre-Matthieu Pair, Softia for EDF.

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

     # Standard Logistic distribution example. 

     print(computeTestKolmogorovLogistic(3.0 + 1.5 * log(1.0 / (1.0 -
     runif(1000)) - 1.0), 3, 1.5))
     print(computeTestKolmogorovLogistic(2.5 + 1.5 * log(1.0 / (1.0 -
     runif(1000)) - 1.0), 3, 1.5))

