slplot              package:pcaMethods              R Documentation

_P_l_o_t _a _s_i_d_e _b_y _s_i_d_e _s_c_o_r_e_s _a_n_d _l_o_a_d_i_n_g_s _p_l_o_t

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

     A common way of representing PCA result for two component

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

     slplot(object, pcs=c(1,2), scoresLoadings=c(TRUE, TRUE),
     sl="def", ll="def", hotelling=0.95, rug=TRUE, sub=NULL,...)

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

  object: a pcaRes object

     pcs: which two pcs to plot

scoresLoadings: Which should be shown scores and or loadings

      sl: labels to plot in the scores  plot

      ll: labels to plot in the loadings plot

hotelling: confidence interval for ellipse

     rug: logical, rug x axis or not

     sub: Subtitle, defaults to annotate with amount of explained
          variance.

     ...: Further arguments to plot functions

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

     Uses layout instead of par to provide side-by-side so it works
     with Sweave.

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

     None, used for side effect.

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

     Henning Redestig

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

     'prcomp', 'pca', 'princomp'

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

     data(iris)
     pcIr <- pca(iris[,1:4], scale="UV", method="svd")
     slplot(pcIr, sl=NULL, pch=5, col=as.integer(iris[,5]))

