attribute            package:verification            R Documentation

_A_t_t_r_i_b_u_t_e _p_l_o_t

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

     An attribute plot  illustrates the reliability, resolution and
     uncertainty of a forecast with respect to the observation. The
     frequency of binned forecast probabilities are plotted against
     proportions of binned observations.  A perfect forecast would be
     indicated by a line plotted along the 1:1 line.  Uncertainty is
     described as the vertical distance between this point and the 1:1
     line.  The relative frequency for each forecast value is displayed
     in parenthesis.

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

     ## Default S3 method:
     attribute(x, obar.i,  prob.y, obar = NULL, titl =
     NULL, ...)
     ## S3 method for class 'prob.bin':
     attribute(x, ...)
            

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

       x: A vector of forecast probabilities or a ``prob.bin'' class
          object produced by the 'verify' function. 

  obar.i: A vector of observed relative frequency of forecast bins.

  prob.y: Relative frequency of forecasts of forecast bins. 

    obar: Climatological or sample mean of observed events.

    titl: Plot title. 

     ...: Graphical parameters

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

     Matt Pocernich <pocernic@rap.ucar.edu>

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

     Hsu, W. R., and A.H. Murphy, 1986: The attributes diagram: A
     geometrical framework for assessing the quality of probability
     forecasts.  _Int. J. Forecasting_ *2*, 285-293.

     Wilks, D. S. (1995) _Statistical Methods in the Atmospheric
     Sciences _ Chapter 7, San Diego: Academic Press.

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

     'verify'

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

     ## Data from Wilks, table 7.3 page 246.
      y.i   <- c(0,0.05, seq(0.1, 1, 0.1))
      obar.i <- c(0.006, 0.019, 0.059, 0.15, 0.277, 0.377, 0.511, 
                  0.587, 0.723, 0.779, 0.934, 0.933)
      prob.y<- c(0.4112, 0.0671, 0.1833, 0.0986, 0.0616, 0.0366,
                 0.0303,  0.0275, 0.245, 0.022, 0.017, 0.203) 
      obar<- 0.162
      
     attribute(y.i, obar.i, prob.y, obar, titl = "Sample Attribute Plot")  

     ## Function will work with a ``prob.bin'' class objects as well.
     ## Note this is a random forecast.
     obs<- round(runif(100))
     pred<- runif(100)

     A<- verify(obs, pred, frcst.type = "prob", obs.type = "binary")

     attribute(A, titl = "Alternative plot", xlab = "Alternate x label" )

