pamr.predictmany            package:pamr            R Documentation

_A _f_u_n_c_t_i_o_n _g_i_v_i_n_g _p_r_e_d_i_c_t_i_o_n _i_n_f_o_r_m_a_t_i_o_n _f_o_r _m_a_n_y _t_h_r_e_s_h_o_l_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 prediction information for many threshold
     values, from a nearest shrunken centroid fit

_U_s_a_g_e:

     pamr.predictmany(fit, newx, threshold=fit$threshold, prior =fit$prior, threshold.scale = fit$
             threshold.scale, ...)

_A_r_g_u_m_e_n_t_s:

     fit: The result of a call to pamr.train 

    newx: Matrix of features at which predictions are to be made

threshold: The desired threshold values

   prior: Prior probabilities for each class. Default is that specified
          in "fit"

threshold.scale: Additional scaling factors to be applied to the
          thresholds. Vector of length equal to the number of classes.
          Default is that specified in "fit".

     ...: Additional arguments to be passed to pamr.predict

_D_e_t_a_i_l_s:

_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)

     pamr.predictmany(mytrain, mydata$x)
      

