pamr.confusion             package:pamr             R Documentation

_A _f_u_n_c_t_i_o_n _g_i_v_i_n_g _a _t_a_b_l_e _o_f _t_r_u_e _v_e_r_s_u_s _p_r_e_d_i_c_t_e_d _v_a_l_u_e_s,
_f_r_o_m _a _n_e_a_r_e_s_t _s_h_r_u_n_k_e_n _c_e_n_t_r_o_i_d _f_i_t.

_D_e_s_c_r_i_p_t_i_o_n:

     A function giving a table of true versus predicted values, from a
     nearest shrunken centroid fit.

_U_s_a_g_e:

     pamr.confusion(fit, threshold, extra=TRUE)

_A_r_g_u_m_e_n_t_s:

     fit: The result of a call to pamr.train or pamr.cv

threshold: The desired threshold value

   extra: If TRUE, class confusion rates are also computed

_D_e_t_a_i_l_s:

     'pamr.confusion' Gives a cross-tabulation of true versus predicted
     classes for the fit returned by pamr.train or pamr.cv, at the
     specified threshold.

_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=y)
     mytrain <-   pamr.train(mydata)
     mycv <- pamr.cv(mytrain,mydata)
     pamr.confusion(mytrain,  threshold=2)
     pamr.confusion(mycv,  threshold=2)
      

