crps              package:verification              R Documentation

_C_o_n_t_i_n_u_o_u_s _R_a_n_k_e_d _P_r_o_b_a_b_i_l_i_t_y _S_c_o_r_e

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

     Calculates the crps for a forecast made in terms of a normal
     probability distribution and an observation expressed in terms of
     a continuous variable.

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

         crps(obs, pred, ...)
            

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

     obs: A vector of observations.

    pred: A vector or matrix of the mean and standard deviation of a
          normal distribution.  If the vector has a length of 2, it is
          assumed that these values represent the mean and standard
          deviation of the normal distribution that will be used for
          all forecasts.

     ...: Optional arguments

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

    crps: Continous ranked probability scores

    CRPS: Mean of crps

     ign: Ignorance score

     IGN: Mean of the ignorance score

_N_o_t_e:

     This function is used within 'verify'.

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

     Matt Pocernich <pocernic@rap.ucar.edu>

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

     Gneiting, T., Westveld, A., Raferty, A. and Goldman, T, 2004:
     _Calibrated Probabilistic Forecasting Using Ensemble Model Output
     Statistics and Minimum CRPS Estimation._ Technical Report no. 449,
     Department of Statistics, University of Washington. [ Available
     online at <URL:
     http://www.stat.washington.edu/www/research/reports/> ]

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

     #  probabilistic/ binary example

     x <- runif(100) ## simulated observation.
     crps(x, c(0,1))

     ## simulated forecast in which mean and sd differs for each forecast.
     frcs<- data.frame( runif(100, -2, 2), runif(100, 1, 3 ) )
     crps(x, frcs)

