pamr.fdr                package:pamr                R Documentation

_A _f_u_n_c_t_i_o_n _t_o _e_s_t_i_m_a_t_e _f_a_l_s_e _d_i_s_c_o_v_e_r_y _r_a_t_e_s _f_o_r _t_h_e _n_e_a_r_e_s_t _s_h_r_u_n_k_e_n _c_e_n_t_r_o_i_d
_c_l_a_s_s_i_f_i_e_r

_D_e_s_c_r_i_p_t_i_o_n:

     A function to estimate false discovery rates for the nearest
     shrunken centroid  classifier

_U_s_a_g_e:

     pamr.fdr(trained.obj, data,  nperms=100, 
      xl.mode=c("regular","firsttime","onetime","lasttime"),xl.time=NULL, xl.prevfit=NULL)

_A_r_g_u_m_e_n_t_s:

trained.obj: The result of a call to pamr.train

    data: Data object; same as the one  passed to pamr.train

  nperms: Number of permutations for estimation of FDRs.  Default is
          100

 xl.mode: Used by Excel interface

 xl.time: Used by Excel interface

xl.prevfit: Used by Excel interface

_D_e_t_a_i_l_s:

     'pamr.fdr' estimates false discovery rates  for a nearest shrunken
     centroid classifier

_V_a_l_u_e:

     A list with components: 

 results: Matrix of estimates FDRs for various various threshold
          values. Reported are both the median and 90th percentile of
          the  FDR over permutations

     pi0: The estimated proportion of genes that are null, i.e. not
          significantly different

_A_u_t_h_o_r(_s):

     Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and
     Gilbert Chu

_R_e_f_e_r_e_n_c_e_s:

_E_x_a_m_p_l_e_s:

     set.seed(120)
     x <- matrix(rnorm(1000*20),ncol=20)
     y <- sample(c(1:4),size=20,replace=TRUE)

     mydata <- list(x=x,y=factor(y), geneid=as.character(1:nrow(x)), genenames=paste("g",as.character(1:nrow(x)),sep=""),)

     mytrain <-   pamr.train(mydata)
     myfdr <- pamr.fdr(mytrain, mydata)

