pcc             package:PresenceAbsence             R Documentation

_P_e_r_c_e_n_t _C_o_r_r_e_c_t_l_y _C_l_a_s_s_i_f_i_e_d

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

     'pcc' calculates the percent correctly classified and (optionally)
     the associated standard deviation from a confusion matrix.

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

     pcc(CMX, st.dev = TRUE)

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

     CMX: a confusion matrix - output from 'cmx' 

  st.dev: a logical indicating whether the associated standard
          deviation should be calculated 

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

     Percent Correctly Classified is simply the proportion of test
     observations that are correctly classified.

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

     if 'st.dev' = FALSE, returns: 'PCC' percent correctly classified. 

     if 'st.dev' = TRUE, returns a dataframe where:

       [1,1]  'PCC'     percent correctly classified
       [1,2]  'PCC.sd'  standard deviation of 'PCC'

_N_o_t_e:

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

     Elizabeth Freeman eafreeman@fs.fed.us

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

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

     'cmx', 'sensitivity', 'specificity', 'Kappa', 'auc'

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

     data(SIM3DATA)

     pcc(cmx(SIM3DATA))

     pcc(cmx(SIM3DATA),st.dev=FALSE)

