plineplot                package:klaR                R Documentation

_P_l_o_t_t_i_n_g _m_a_r_g_i_n_a_l _p_o_s_t_e_r_i_o_r _c_l_a_s_s _p_r_o_b_a_b_i_l_i_t_i_e_s

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

     For a given variable the posteriori probabilities of the classes
     given by a  classification method are plotted. The variable need
     not be used for the actual classifcation.

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

     plineplot(formula, data, method, x, col.wrong = "red", ylim = c(0, 1), loo=FALSE, ...)

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

 formula: formula of the form 'groups ~ x1 + x2 + ...'.  That is, the
          response is the grouping factor and the right hand side
          specifies the (non-factor) discriminators.

    data: Data frame from which variables specified in formula are
          preferentially to be taken.

  method: character, name of classification function  (e.g. "'lda'").

       x: variable that should be plotted. See examples.

col.wrong: color to use for missclassified objects.

    ylim: 'ylim' for the plot.

     loo: Should leave-one-out estimate be used for prediction?

     ...: further arguments passed to the underlying classification
          method or plot functions.

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

     The actual error rate.

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

     Karsten Luebke

_S_e_e _A_l_s_o:

     'partimat'

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

     library(MASS)

     # The name of the variable can be used for x
     data(B3)
     plineplot(PHASEN ~ ., data = B3, method = "lda", 
         x = "EWAJW", xlab = "EWAJW")

     # The plotted variable need not be in the data
     data(iris)
     iris2 <- iris[ , c(1,3,5)]
     plineplot(Species ~ ., data = iris2, method = "lda", 
         x = iris[ , 4], xlab = "Petal.Width")

