

_P_r_i_n_c_i_p_a_l _C_o_m_p_o_n_e_n_t_s _A_n_a_l_y_s_i_s

     prcomp(x=, scale=FALSE, use="all.obs")

     print.prcomp(prcomp.obj)
     plot.prcomp(prcomp.obj)

_A_r_g_u_m_e_n_t_s:

       x: a matrix (or data frame) which provides the data
          for the principal components analysis.

   scale: a logical value indicating whether the variables
          should be scaled to have unit variance before the
          analysis takes place.

     use: the strategy to use for dealing with missing
          observations.  The possible values are
          `"all.obs"', `"complete.obs"', or
          `"pairwise.complete.obs"'.  An unambiguous sub-
          string can be used.

_D_e_s_c_r_i_p_t_i_o_n:

     This function performs a principal components analysis
     on the given data matrix and returns the results as a
     `prcomp' object.  The print method for the these
     objects prints the results in a nice format and the
     plot method produces a scree plot.

_V_a_l_u_e:

     `prcomp' returns an list with class `"prcomp"' contain-
     ing the following components:

     var: the variances of the principal components (i.e.
          the eigenvalues)

    load: the matrix of variable loadings (i.e. a matrix
          whose columns contain the eigenvectors).

   scale: the value of the `scale' argument.

_R_e_f_e_r_e_n_c_e_s:

     Mardia, K. V., J. T. Kent and J. M. Bibby (1979).  Mul-
     tivariate Analysis, London: Academic Press.

_S_e_e _A_l_s_o:

     `cor', `cov', `eigen'.

_E_x_a_m_p_l_e_s:

     # the variances of the variables in the
     # crimes data vary by orders of magnitude
     data(crimes)
     prcomp(crimes)
     prcomp(crimes,scale=TRUE)

