disjoin               package:pastecs               R Documentation

_C_o_m_p_l_e_t_e _d_i_s_j_o_i_n_e_d _c_o_d_e_d _d_a_t_a (_b_i_n_a_r_y _c_o_d_i_n_g)

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

     Transform a factor in separate variables (one per level) with a
     binary code (0 for absent, 1 for present) in each variable

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

     disjoin(x)

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

       x: a vector containing a factor data 

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

     Use 'cut()' to transform a numerical variable into a factor
     variable

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

     a matrix containing the data with binary coding

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

     Frdric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean
     (phgrosjean@sciviews.org)

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

     Fromentin J.-M., F. Ibanez & P. Legendre, 1993. _A
     phytosociological method for interpreting plankton data._ Mar.
     Ecol. Prog. Ser., 93:285-306.

     Gebski, V.J., 1985. _Some properties of splicing when applied to
     non-linear smoothers._ Comput. Stat. Data Anal., 3:151-157.

     Grandjouan, G., 1982. _Une mthode de comparaison statistique
     entre les rpartitions des plantes et des climats._ Thse d'Etat,
     Universit Louis Pasteur, Strasbourg.

     Ibanez, F., 1976. _Contribution  l'analyse mathmatique des
     vnements en Ecologie planctonique. Optimisations
     mthodologiques._ Bull. Inst. Ocanogr. Monaco, 72:1-96.

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

     'buysbal', 'cut'

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

     # Artificial data with 1/5 of zeros
     Z <- c(abs(rnorm(8000)), rep(0, 2000))
     # Let the program chose cuts
     table(cut(Z, breaks=5))
     # Create one class for zeros, and 4 classes for the other observations
     Z2 <- Z[Z != 0]
     cuts <- c(-1e-10, 1e-10, quantile(Z2, 1:5/5, na.rm=TRUE))
     cuts
     table(cut(Z, breaks=cuts))
     # Binary coding of these data
     disjoin(cut(Z, breaks=cuts))[1:10, ]

