biplot.princomp package:stats 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' or 'prcomp' _U_s_a_g_e: ## S3 method for class 'prcomp': biplot(x, choices = 1:2, scale = 1, pc.biplot = FALSE, ...) ## S3 method for class 'princomp': biplot(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: require(graphics) biplot(princomp(USArrests))