MFA                package:FactoMineR                R Documentation

_M_u_l_t_i_p_l_e _F_a_c_t_o_r _A_n_a_l_y_s_i_s (_M_F_A)

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

     Performs Multiple Factor Analysis (MFA) with supplementary
     individuals and supplementary groups of variables. Groups of
     variables can be quantitative or qualitative.

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

     MFA (base, group, type = rep("s",length(group)), ind.sup = NULL, 
         ncp = 5, name.group = NULL, num.group.sup = NULL, 
         graph = TRUE, weight.col.mfa = NULL)

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

    base: a data frame with _n_ rows (individuals) and _p_ columns
          (variables)

   group: a list indicating the number of variables in each group

    type: the type of variables in each group; three possibilities: "c"
          or "s" for quantitative variables (the difference is that for
          "s" variables are scaled to unit variance), "n" for
          qualitative variables; by default, all variables are
          quantitative and scaled to unit variance

 ind.sup: a vector indicating the indexes of the supplementary
          individuals

     ncp: number of dimensions kept in the results (by default 5)

name.group: a vector containing the name of the groups (by default,
          NULL and the group are named group.1, group.2 and so on)

num.group.sup: the indexes of the illustrative groups (by default, NULL
          and no group are illustrative)

   graph: boolean, if TRUE a graph is displayed

weight.col.mfa: vector of weights, useful for HMFA method (by default,
          NULL and an MFA is performed)

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

summary.quali: a summary of the results for the qualitative variables

summary.quanti: a summary of the results for the quantitative variables

separate.analyses: the results for the separate analyses

     eig: a numeric vector containing all the eigenvalues

   group: a list of matrices containing all the results for the groups
          (Lg and RV coefficients, coordinates, square cosine,
          contributions, distance to the origin, the correlations
          between each group and each factor)

rapport.inertie: inertia ratio

     ind: a list of matrices containing all the results for the active
          individuals (coordinates, square cosine, contributions)

 ind.sup: a list of matrices containing all the results for the
          supplementary individuals (coordinates, square cosine)

quanti.var: a list of matrices containing all the results for the
          quantitative variables (coordinates, correlation between
          variables and axes)

quali.var: a list of matrices containing all the results for the
          supplementary qualitative variables (coordinates of each
          categories of each variables, and v.test which is a criterion
          with a Normal distribution)

partial.axes: a list of matrices containing all the results for the
          partial axes (coordinates, correlation between variables and
          axes, correlation between partial axes)


     Returns the individuals factor map, the variables factor map and
     the groups factor map.

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

     Jrmy Mazet, Franois Husson Franois.Husson@agrocampus-rennes.fr

_R_e_f_e_r_e_n_c_e_s:

     Escofier, B. and Pags, J. (1994) Multiple Factor Analysis (AFMULT
     package), _Computational Statistics and Data Analysis_, *18*,
     121-140.

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

     'print.MFA', 'plot.MFA'

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

     data(wine)
     aa = MFA(wine, group=c(2,5,3,10,9,2), type=c("n",rep("s",5)),
         ncp=5, name.group=c("orig","olf","vis","olfag","gust","ens"),
         num.group.sup=c(1,6))

     ## Not run: 
     #### Interactive graph
     liste = plot.MFApartial(aa)
     plot(aa,choix="ind",habillage = "quali")
     ## End(Not run)

     ###Example 2
     data (poison)
     MFA(poison, group=c(2,2,5,6), type=c("s","n","n","n"),
         name.group=c("desc","desc2","symptom","eat"),
         num.group.sup=1:2)

