quantV                 package:qualV                 R Documentation

_Q_u_a_n_t_i_t_a_t_i_v_e _V_a_l_i_d_a_t_i_o_n _M_e_t_h_o_d_s

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

     Different methods for calculating the difference between two
     vectors.

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

     generalME(o, p,
               ignore   = c("raw", "centered", "scaled", "ordered"),
               geometry = c("real", "logarithmic", "geometric", "ordinal"),
               measure  = c("mad", "var", "sd"),
               type     = c("dissimilarity", "normalized", "similarity",
                            "reference", "formula", "name", "function"),
                            method = NULL)
        MAE(o, p, type = "dissimilarity")
       MAPE(o, p, type = "dissimilarity")
        MSE(o, p, type = "dissimilarity")

       RMSE(o, p, type = "dissimilarity")
       CMAE(o, p, type = "dissimilarity")
       CMSE(o, p, type = "dissimilarity")
      RCMSE(o, p, type = "dissimilarity")
       SMAE(o, p, type = "dissimilarity")
       SMSE(o, p, type = "dissimilarity")
      RSMSE(o, p, type = "dissimilarity")
       MALE(o, p, type = "dissimilarity")
       MAGE(o, p, type = "dissimilarity")
      RMSLE(o, p, type = "dissimilarity")
      RMSGE(o, p, type = "dissimilarity")

      SMALE(o, p, type = "dissimilarity")
      SMAGE(o, p, type = "dissimilarity")
      SMSLE(o, p, type = "dissimilarity")

     RSMSLE(o, p, type = "dissimilarity")
     RSMSGE(o, p, type = "dissimilarity")

       MAOE(o, p, type = "dissimilarity")
       MSOE(o, p, type = "dissimilarity")
      RMSOE(o, p, type = "dissimilarity")

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

       o: vector of observed values

       p: vector of corresponding predicted values

    type: one of 'c("dissimilarity", "normalized", "similarity",
          "reference", "formula")', for the dissimilarity measure, the
          normalized dissimilarity measure, the similarity measure, or
          the formula for the normalized measure. For 'generalME' it is
          additionally possible to specify '"function"' for getting the
          corresponding function and '"name"' for getting the name of
          the function. 

  ignore: specifies which aspects should be ignored: "raw" compares
          original values, "centered" removes differences in mean,
          "scaled" ignores scaling, "ordered" indicates the use of the
          ordinal geometry only.

geometry: indicating the geometry to be used for the data and the
          output, "real" corresponds to arithmetic differences and
          means, "logarithmic" to handling relative data on a
          logarithmic scale, "geometric" to geometric means and
          differences and "ordinal" to a pure ordinal treatment.

 measure: indicates how distances should be measured: as mean absolute
          distances like in MAD, as squared distances like in a
          variance, or as the root of mean squared distances like in
          sd.

  method: optionally the function to be used can specified directly as
          a function or as a string.

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

     These comparison criteria are designed for a semiquantitative
     comparison of observed values 'o' with predicted values 'p' to
     validate the performance of the prediction. 
      The general naming convention follows the grammar scheme 
      '[R][C|S]M[S|A][L|G|O]E' 
      corresponding to '[Root] [Centered | Scaled] Mean [Squared |
     Absolute]' 
      '[Logarithmic, Geometric, Ordinal] Error'

_R_o_o_t is used together with squared errors to indicate, that a root is
     applied to the mean.

_C_e_n_t_e_r_e_d indicates that an additive constant is allowed.

_S_c_a_l_e_d indicates that a scaling of the predictive sequence is allowed.
     Scaled implies centered for real scale.

_S_q_u_a_r_e_d indicates that squared error is used. 

_A_b_s_o_l_u_t_e indicates that absolute error is used.

_L_o_g_a_r_i_t_h_m_i_c indicates that the error is calculated based on the
     logarithms of the values. This is useful for data on a relative
     scale.

_G_e_o_m_e_t_r_i_c indicates that the result is to be understood as a factor,
     similar to a geometric mean.

_O_r_d_i_n_a_l indicates that only the order of the observations is taken into
     account by analyzing the data by ranks scaled to the interval [0,
     1].

     The mean errors for squared error measures are based on the number
     of degrees of freedom of the residuals.

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

generalME: selects the best deviance measure according to the
          description given in the parameters. It has the two
          additional possibilities of name and function in the type
          parameter.

     MAE: mean absolute error 1/n

    MAPE: mean absolute percentage error

     MSE: mean squared error

    RMSE: root mean squared error

    CMAE: centered mean absolute error

    CMSE: centered mean squared error

   RCMSE: root centered mean squared error

    SMAE: scaled mean absolute error

    SMSE: scaled mean squared error

   RSMSE: root scaled mean squared error

    MALE: mean absolute logarithmic error

    MAGE: mean absolute geometric error

    MSLE: mean squared logarithmic error

    MSGE: mean squared geometric error

   RMSLE: root mean squared logarithmic error

   SMALE: scaled mean absolute logarithmic error

   SMAGE: scaled mean absolute relative error

   SMSLE: scaled mean squared logarithmic error

  RSMSLE: root scaled mean squared logarithmic error

  RSMSGE: root scaled mean squared geometric error

    MAOE: mean absolute ordinal error 

    MSOE: mean squared ordinal error

   RMSOE: root mean squared ordinal error

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

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

     Jachner, S., K.G. v.d. Boogaart, T. Petzoldt (2007) Statistical
     methods for the qualitative assessment of dynamic models with time
     delay (R package qualV), in preparation

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

     'EF', 'GRI', 'compareME'

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

     data(phyto)
     obsb <- na.omit(obs[match(sim$t, obs$t), ])
     simb <- sim[na.omit(match(obs$t, sim$t)), ]
     o <- obsb$y
     p <- simb$y

     generalME(o, p, ignore = "raw", geometry = "real")

        MAE(o, p)
       MAPE(o, p)
        MSE(o, p)
       RMSE(o, p)
       CMAE(o, p)
       CMSE(o, p)
      RCMSE(o, p)
       SMAE(o, p)
       SMSE(o, p)
      RSMSE(o, p)
       MALE(o, p)
       MAGE(o, p)
      RMSLE(o, p)
      RMSGE(o, p)

      SMALE(o, p)
      SMAGE(o, p)
      SMSLE(o, p)

     RSMSLE(o, p)
     RSMSGE(o, p)

       MAOE(o, p)
       MSOE(o, p)
      RMSOE(o, p)
        MAE(o, p)
       MAPE(o, p)

        MSE(o, p, type = "s")
       RMSE(o, p, type = "s")
       CMAE(o, p, type = "s")
       CMSE(o, p, type = "s")
      RCMSE(o, p, type = "s")
       SMAE(o, p, type = "s")
       SMSE(o, p, type = "s")
      RSMSE(o, p, type = "s")
       MALE(o, p, type = "s")
       MAGE(o, p, type = "s")
      RMSLE(o, p, type = "s")
      RMSGE(o, p, type = "s")

      SMALE(o, p, type = "s")
      SMAGE(o, p, type = "s")
      SMSLE(o, p, type = "s")

     RSMSLE(o, p, type = "s")
     RSMSGE(o, p, type = "s")

       MAOE(o, p, type = "s")
       MSOE(o, p, type = "s")
      RMSOE(o, p, type = "s")

