ROC.FCS               package:rflowcyt               R Documentation

_R_O_C (_R_e_c_e_i_v_e_r _O_p_e_r_a_t_i_n_g _C_h_a_r_a_c_t_e_r_i_s_t_i_c) _C_u_r_v_e: 
_P_e_r_c_e_n_t_a_g_e _P_o_s_i_t_i_v_e_s _f_o_r _F_l_o_w _C_y_t_o_m_e_t_r_y _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     This function plots an ROC curve based on cutoff values from the
     observed combined dataset of hivpos and hivneg, which both are
     vectors of patient-specific percentage positives based on the
     99.9th percentile of the corresponding control sample
     distribution.  The output contains the sensitivities,
     1-specificity,and the observed dataset, cutoff values.

_U_s_a_g_e:

     ROC.FCS(hivpos, hivneg, lineopt = 1, colopt = 1, overlay = FALSE)

_A_r_g_u_m_e_n_t_s:

  hivpos: numerical vector of percentage positives for the HIV positive
          individuals/samples for a given condition

  hivneg: numerical vector of the percentage positives for the HIV
          negative individuals/samples for a given condition

 lineopt: numerical value for the lty option of the plot  (line type)

  colopt: numerical value for the col option of the plot (color type)

 overlay: Boolean expression as to whether or not the plot is an
          overlay

_D_e_t_a_i_l_s:

     See 'PerPosROC' in the 'rfcdorig' package for a description of the
     input data and how percentage positives are defined.

     The ROC curve in the example demonstrates that there is higher
     predictive ability of using the GAG stimulated samples rather than
     the PolA or PolB stimulated samples.

_V_a_l_u_e:

     Let T be the the percentage positives, c be a given value in
     c.obs, and HIV+ defined as among HIV positive individuals, and
     HIV- defined as among HIV negative individuals.

sensitivity: numerical vector of the sensitivity=P(T>c | HIV+)
          calculated corresponding to a given cut-off in c.obs

spec.complement: numerical vector of 1-specificity= P(T>c | HIV
          -)corresponding to a given cut-off in c.obs

   c.obs: a numerical vector of the cutoffs which were taken to be the
          values of the observations (the values of the percentage
          positives  of both the HIV positive and HIV negative data)

_A_u_t_h_o_r(_s):

     A.J. Rossini and J.Y. Wan

_R_e_f_e_r_e_n_c_e_s:

     Zoe Moodie and Mario Roederer

_S_e_e _A_l_s_o:

     'PercentPos.FCS', data 'PerPosROC' in 'rfcdorig' package,
     'percentile.FCS'

_E_x_a_m_p_l_e_s:

     if (require(rfcdmin)){

     data(PerPosROCmin)

     #plotting the gag stimulated 100* percent positives
     if (interactive()==TRUE){
     GAG<-ROC.FCS(hivpos.gag, hivneg.gag)
     #plotting the pola stimulated 100* percent positives
     POLA<-ROC.FCS(hivpos.pola, hivneg.pola, lineopt=2, colopt=2, overlay=TRUE)
     #plotting the polb stimulated 100* percent positives
     POLB<-ROC.FCS(hivpos.polb, hivneg.polb, lineopt=4, colopt=3, overlay=TRUE)
     legend(0.7, 0.7, c("gag", "polA", "polB"), col = c(1,2,3), lty=c(1,2,4))
     }

     }

