computeTestKolmogorovWeibull   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 _W_e_i_b_u_l_l _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
     Weibull distribution of the given parameters and returns a list
     containing the result and test p-value.

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

     computeTestKolmogorovWeibull(numericalSample, alpha, beta, gamma,
     testLevel = 0.95, estimatedParameters)

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

numericalSample: the sample to be tested (numeric vector)

   alpha: The Weibull distribution alphaParameter.

    beta: The Weibull distribution betaParameter.

   gamma: The Weibull distribution gammaParameter.

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 Weibull distribution example. 
     sample <- 3.0 * (-log(runif(1000)))^(1 / 5)
     print(computeTestKolmogorovWeibull(sample, 3, 5, 0))
     print(computeTestKolmogorovWeibull(sample, 2.5, 5, 0))

     # Non - Standard Weibull distribution example. 

     print(computeTestKolmogorovWeibull(sample + 1, 3, 5, 1))
     print(computeTestKolmogorovWeibull(sample + 1, 3, 5, 0.5))

