value              package:verification              R Documentation

_F_o_r_e_c_a_s_t _V_a_l_u_e _F_u_n_c_t_i_o_n

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

     Calculates the economic value of a forecast based on a cost/loss
     ratio.

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

     value(obs, pred= NULL, baseline = NULL, cl = seq(0.05, 0.95, 0.05),             plot = TRUE, all = FALSE, thresholds = seq(0.05, 0.95, 0.05), ylim = c(-0.1, 0.5), xlim = c(0,1), ...)

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

     obs: A vector of binary observations or a contingency table
          summary of values in the form c(n11, n01, n10, n00) where in 
          nab a = obs, b = forecast.

    pred: A vector of probablistic predictions.

baseline: Baseline  or naive forecast.  Typically climotology.

      cl: Cost loss ratio.  The relative value of being unprepared and
          taking a loss to that of un-necessarily preparing.  For
          example,  cl = 0.1 indicates it would cost $ 1 to prevent a
          $10 loss.  This defaults to the sequence 0.05 to 0.95  by
          0.05.  

    plot: Should a plot be created? Default is TRUE

     all: In the case of probablistic forecasts, should value curves
          for each thresholds be displayed.

thresholds: Thresholds considered for a probalistic forecast.

ylim, xlim: Plotting options.

     ...: Options to be passed into the plotting function.

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

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

    vmax: Maximum value

       V: Vector of values for each cl value

       F: Conditional false alarm rate.

       H: Conditional hit rate

      cl: Vector of cost lost ratios.

       s: Base rate

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

     Matt Pocernich <pocernic@rap.ucar.edu>

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

     Jolliffe, Ian and David B. Stephensen (2003) _Forecast
     Verification: A Practioner's Guide in Atmospheric Science_,
     Chapter 8. Wiley

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

     ## value as a contigency table
     ## Finley tornado data

     obs<- c(28, 72, 23, 2680) 
     value(obs)
     aa <- value(obs)
     aa$Vmax # max value

     ## probablistic forecast example
      obs  <- round(runif(100) )
      pred <-  runif(100)

     value(obs, pred, main = "Sample Plot",
                  thresholds = seq(0.02, 0.98, 0.02) ) 

      

