PLR               package:MCRestimate               R Documentation

_A _f_u_n_c_t_i_o_n _w_h_i_c_h _p_e_r_f_o_r_m_s _p_e_n_a_l_i_s_e_d _l_o_g_i_s_t_i_c _r_e_g_r_e_s_s_i_o_n
_c_l_a_s_s_i_f_i_c_a_t_i_o_n _f_o_r _t_w_o _g_r_o_u_p_s

_D_e_s_c_r_i_p_t_i_o_n:

     A function which performs penalised logistic regression.

_U_s_a_g_e:

     PLR(trainmatrix, resultvector, kappa=0, eps=1e-4)
            predict.PLR(object,...)

_A_r_g_u_m_e_n_t_s:

resultvector: a vector which contains the labeling of the samples

trainmatrix: a matrix which includes the data. The rows corresponds to
          the observations and the colums to the variables.

   kappa: value range for penalty parameter. If more that one parameter
          is specified the one with the lowest AIC will be used.

     eps: 

  object: a fitted PLR model

     ...: here a data matrix from samples that should be predicted

_D_e_t_a_i_l_s:

_V_a_l_u_e:

     a list with three arguments 

       a: Intercept estimate of the linear predictor

       b: vector of estimated regresion coefficients

factorlevel: levels of grouping variable

    aics: vector of AIC values with respect to penalty parameter kappa

     trs: vector of effective degrees of freedom with respect to
          penalty parameter kappa

_A_u_t_h_o_r(_s):

     Axel Benner, Ulrich Mansmann, based on MathLab code by Paul Eilers

_E_x_a_m_p_l_e_s:

     library(golubEsets)
     data(Golub_Merge)
     eSet<-Golub_Merge
     X0 <- t(exprs(eSet))
     m <- nrow(X0); n <- ncol(X0)
     y <- pData(eSet)$ALL.AML
     f <- PLR(X0, y,kappa=10^seq(0, 7, 0.5))
     if (interactive()) {
       x11(width=9, height=4)
       par(mfrow=c(1,2))
     plot(log10(f$kappas), f$aics, type="l",main="Akaike's Information Criterion", xlab="log kappa", ylab="AIC")
     plot(log10(f$kappas), f$trs, type="l",xlab="log kappa",
     ylab="Dim",main="Effective dimension")
     }

