FDA                package:FactoMineR                R Documentation

_F_a_c_t_o_r_i_a_l _D_i_s_c_r_i_m_i_n_a_n_t _A_n_a_l_y_s_i_s

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

     Performs Factorial Discriminant Analysis (FDA).

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

     FDA(X, fact, new.data = NULL, new.fact = NULL, prior = NULL, 
         cross.val = FALSE, graph = TRUE)

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

X         : a data frame with n rows (individuals) and p columns
          (including one factor). 

fact      : a factor specifying the class for each observation.

new.data  : a data frame of individuals to be classified or, if
          'new.fact' is not null, which formed the test sample.  

new.fact  : if 'new.dat' is not null, a factor specifying the class for
          each observation of the test sample.   

prior     : a vector. The prior probabilities of class membership.  If
          unspecified, the class proportions for the training sample
          ('new.dat') are used.  

cross.val : if TRUE, returns results for leave-one-out
          cross-validation. 

   graph: boolean, if TRUE graphs are plotted

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

     If there is no test sample, the evaluation of the affectation
     model is realised on the test sample itself.

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

     Returns a list including :

    eig : a numeric vector containing all the  eigenvalues 

eigen.vectors : a list of matrices containing all the eigenvectors

var      : a list of matrices containing all the results for the active
          variables  

cg       : a list of matrices containing all the results for the
          centers of gravity 

ind      : a list of matrices containing all the results for the
          individuals 

call         : a list with the data frame and grouping factor used

df           : the discriminant functions

score        : a vector whith the individuals scores.

eval         : a list with all the results for the evaluation of the
          affectation model

res.cv       : a list with all the results for for leave-one-out
          cross-validation.

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

     Jeremy Mazet jeremy.mazet@soredab.org

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

     'plot.FDA', 'print.FDA'

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

     data(wine)
     res.fda <- FDA(wine[,-(1:28)], fact=wine[,1])

