rocdemo.sca               package:ROC               R Documentation

_f_u_n_c_t_i_o_n _t_o _b_u_i_l_d _o_b_j_e_c_t_s _o_f _c_l_a_s_s '_r_o_c_c'

_D_e_s_c_r_i_p_t_i_o_n:

     rocdemo.sca - demonstrate 'rocc' class construction using a scalar
     marker and simple functional rule

_U_s_a_g_e:

     rocdemo.sca(truth, data, rule=NULL, 
            cutpts=NA,
            markerLabel="unnamed marker", caseLabel="unnamed diagnosis")

_A_r_g_u_m_e_n_t_s:

   truth: true classification of objects. Must take values 0 or 1.

    data: quantitative markers used to classify

    rule: rule: a function with arguments (x, thresh) returning 0 or 1.
          If no rule is provided the standard rule 'dxrule.sca' is
          assumed and a faster implementation utilized.

  cutpts: values of thresholds

markerLabel: textual label describing marker

caseLabel: textual label describing classification

_D_e_t_a_i_l_s:

     dxrule.sca is function (x, thresh) ifelse(x > thresh, 1, 0)

     The default value of argument cutpts is a point less than
     min(data), points separating the unique values of data and a point
     greater than max(data).

_V_a_l_u_e:

     an object of S4 class rocc

_N_o_t_e:

_A_u_t_h_o_r(_s):

     Vince Carey (stvjc@channing.harvard.edu)

_R_e_f_e_r_e_n_c_e_s:

_S_e_e _A_l_s_o:

     AUC

_E_x_a_m_p_l_e_s:

     set.seed(123)
     R1 <- rocdemo.sca( rbinom(40,1,.3), rnorm(40), caseLabel="new case", markerLabel="demo Marker" )
     plot(R1, line=TRUE, show.thresh=TRUE)

     truth <- c(0, 1, 0, 1, 1, 0, 1, 1)
     data <- c(2, 3, 4, 4, 5, 6, 7, 8)
     R2 <- rocdemo.sca(truth, data, dxrule.sca)
     plot(R2, line=TRUE, show.thresh=TRUE)
     R3 <- rocdemo.sca(truth, data, function(x, thresh) 1 - dxrule.sca(x, thresh))
     if (AUC(R2) + AUC(R3) != 1) stop('Sum of AUCs should be 1.')