

_S_c_a_l_i_n_g _a_n_d _C_e_n_t_e_r_i_n_g _o_f _M_a_t_r_i_c_e_s

     scale(x, center=TRUE, scale=TRUE)

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

       x: a numeric matrix.

  center: either a logical value or a numeric vector of
          length equal to the number of columns of `x'.

   scale: either a logical value or a numeric vector of
          length equal to the number of columns of `x'.

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

     The value of `center' determines how column centering
     is performed.  If `center' is a numeric vector with
     length equal to the number of columns of `x', then each
     column of `x' has the corresponding value from `center'
     subtracted from it.  If `center' is `TRUE' then center-
     ing is done by subtracting the column means of `x' from
     their corresponding columns and if `center' is `FALSE',
     no centering is done.

     The value of `scale' determines how column scaling is
     performed (after centering).  If `scale' is a numeric
     vector with length equal to the number of columns of
     `x', then each column of `x' is divided by the
     corresponding value from `scale'.  If `scale' is `TRUE'
     then scaling is done by dividing the (centered) columns
     of `x' by their root-mean-square, and if `scale' is
     `FALSE', no scaling is done.

     The root-mean-square for a column is obtained by com-
     puting the square-root of the sum-of-squares of the
     non-missing values in the column divided by the number
     of non-missing values minus one.

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

     The centered, scaled matrix.

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

     `sweep' which allows centering (and scaling) with arbi-
     trary statistics.

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

     x <- matrix(1:10, nc=2)
     (centered.x <- scale(x, scale=FALSE))
     cov(centered.scaled.x <- scale(x))# all 1

