leps              package:verification              R Documentation

_L_i_n_e_a_r _E_r_r_o_r _i_n _P_r_o_b_a_b_i_l_i_t_y _S_p_a_c_e (_L_E_P_S)

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

     Calculates the linear error in probability spaces. This is the
     mean absolute difference between the forecast cumulative
     distribution value (cdf) and the observation.  This function
     creates the empirical cdf function for the observations using the
     sample population.  Linear interpretation is used to estimate the
     cdf values between observation values.  Therefore; this may
     produce awkward results with small datasets.

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

         ## Default S3 method:
         leps(x, pred, plot = TRUE, ... )
                              

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

       x: A vector of observations or a verification object with
          ``cont.cont'' properties.

    pred: A vector of predictions.

    plot: Logical to generate a plot or not.

     ...: Additional plotting options.

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

     If assigned to an object, the following values are reported. 

  leps.0: Negatively oriented score on the [0,1] scale, where 0 is a
          perfect score.

  leps.1: Positively oriented score proposed by Potts.

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

     Matt Pocernich <pocernic@rap.ucar.edu>

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

     DeQue, Michel. (2003) ``Continuous Variables'' *Chapter 5*,
     _Forecast Verification: A Practitioner's Guide in Atmospheric
     Science._

     Potts, J. M., Folland, C.K., Jolliffe, I.T. and Secton, D. (1996)
     ``Revised `LEPS' scores fore assessing climate model simulations
     and long-range forecasts.'' _J. Climate_, *9*, pp. 34-54.

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

      obs <- rnorm(100, mean = 1, sd = sqrt(50))
      pred<-  rnorm(100, mean = 10, sd = sqrt(500))

      leps(obs, pred) 

      

