EF                   package:qualV                   R Documentation

_E_f_f_i_c_i_e_n_c_y _F_a_c_t_o_r _a_s _S_u_g_g_e_s_t_e_d _b_y _N_a_s_h & _S_u_t_c_l_i_f_f_e

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

     The efficiency factor is a dimensionless statistic which directly
     relates predictions to observed data.

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

     EF(o, p)

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

       o: vector of observed values

       p: vector of corresponding predicted values

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

     Two time series are compared. ''EF'' is an overall measure of
     similarity between fitted and observed values. Any model giving a
     negative value cannot be recommended, whereas values close to one
     indicate a 'near-perfect' fit.

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

      EF: efficiency factor

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

     Nash, J. E. and Sutcliffe, J. V. (1970) River flow forecasting
     through conceptual models part I - A discussion of principles.
     Journal of Hydrology, 10, 282-290.

     Mayer, D. G. and Butler, D. G. (1993) Statistical Validation.
     Ecological Modelling, 68, 21-32.

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

     'MAE', 'MSE', 'MAPE', 'GRI'

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

     # a constructed example
     x <- seq(0, 2*pi, 0.1)
     y <- 5 + sin(x)           # a process
     o <- y + rnorm(x, sd=0.2) # observation with random error
     p <- y + 0.1              # simulation with systematic bias

     plot(x, o); lines(x, p)
     EF(o, p)

     # observed and measured data with non-matching time intervals
     data(phyto)
     obsb <- na.omit(obs[match(sim$t, obs$t), ])
     simb <- sim[na.omit(match(obs$t, sim$t)), ]
     EF(obsb$y, simb$y)

