brier.score           package:iterativeBMA           R Documentation

_B_r_i_e_r _S_c_o_r_e: _a_s_s_e_s_s_m_e_n_t _o_f _p_r_e_d_i_c_t_i_o_n _a_c_c_u_r_a_c_y

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

     The Brier Score is a probabilistic number of errors that takes the
     predicted probabilities into consideration. A small Brier Score
     indicates high prediction accuracy. This function assumes 2-class
     data, and requires the true class labels to be known.

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

     brier.score (predictedArr, truthArr)

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

predictedArr: a vector consisting of the predicted probabilities that
          the test sample belongs to class 1.

truthArr: a zero-one vector indicating the known class labels of the
          test samples. We assume this vector has the same length as
          'predictedArr'.

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

     The Brier Score computes the sum of squares of the differences 
     between the true class and the predicted probability over all 
     test samples. If the predicted probabilities are constrained  to
     equal to 0 or 1, the Brier Score is equal to the total  number of
     classification errors.

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

     A non-negative real number.

_R_e_f_e_r_e_n_c_e_s:

     Brier, G.W. (1950)  Verification of forecasts expressed in terms
     of probability. Monthly Weather Review 78: 1-3.

     Yeung, K.Y., Bumgarner, R.E. and Raftery, A.E. (2005)  Bayesian
     Model Averaging: Development of an improved multi-class, gene
     selection and classification tool for microarray data. 
     Bioinformatics 21: 2394-2402.

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

     'bma.predict', 'iterateBMAglm.train.predict'

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

     library (Biobase)
     library (BMA)
     library (iterativeBMA)
     data(trainData)
     data(trainClass)
     data (testData)

     ret.vec <- iterateBMAglm.train.predict (train.expr.set=trainData, test.expr.set=testData, trainClass, p=100)

     ## compute the Brier Score
     data (testClass)
     brier.score (ret.vec, testClass)

