biplot.princomp             package:mva             R Documentation

_B_i_p_l_o_t _f_o_r _P_r_i_n_c_i_p_a_l _C_o_m_p_o_n_e_n_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Produces a biplot (in the strict sense) from the output of
     `princomp'.

_U_s_a_g_e:

     biplot.princomp(x, choices = 1:2, scale = 1, pc.biplot = FALSE, ...)

_A_r_g_u_m_e_n_t_s:

       x: an object of class `"princomp"'.

 choices: length 2 vector specifying the components to plot. Only the
          default is a biplot in the strict sense. 

   scale: The variables are scaled by `lambda ^ scale' and the
          observations are scaled by `lambda ^ (1-scale)' where
          `lambda' are the singular values as computed by `princomp'.
          Normally `0 <= scale <= 1', and a warning  will be issued if
          the specified `scale' is outside this range. 

pc.biplot: If true, use what Gabriel (1971) refers to as a "principal
          component biplot", with `lambda = 1' and observations scaled
          up by sqrt(n) and variables scaled down by sqrt(n).  Then
          inner products between variables approximate covariances and
          distances between observations approximate Mahalanobis
          distance. 

     ...: optional arguments to be passed to `biplot.default'.

_D_e_t_a_i_l_s:

     This is a method for the generic function `biplot'.  There is
     considerable confusion over the precise definitions: those of the
     original paper, Gabriel (1971), are followed here.  Gabriel and
     Odoroff (1990) use the same definitions, but their plots actually
     correspond to `pc.biplot = TRUE'.

_S_i_d_e _E_f_f_e_c_t_s:

     a plot is produced on the current graphics device.

_R_e_f_e_r_e_n_c_e_s:

     Gabriel, K. R. (1971). The biplot graphical display of matrices
     with applications to principal component analysis. Biometrika, 58,
     453-467.

     Gabriel, K. R. and Odoroff, C. L. (1990). Biplots in biomedical
     research. Statistics in Medicine, 9, 469-485.

_S_e_e _A_l_s_o:

     `biplot', `princomp'.

_E_x_a_m_p_l_e_s:

     data(USArrests)
     biplot(princomp(USArrests))

