leverage             package:pcaMethods             R Documentation

_E_x_t_r_a_c_t _l_e_v_e_r_a_g_e_s _o_f _a _P_C_A _m_o_d_e_l

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

     The leverages of PCA model indicate how much influence each
     observation has on the PCA model. Observations with high leverage
     has caused the principal components to rotate towards them. It can
     be used to extract both "unimportant" observations as well as
     picking potential outliers.

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

     leverage(object,...)

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

  object: a pcaRes object

     ...: not used

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

     Defined as Tr(T(T'T)^(-1)T')

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

     The observation leverages as a numeric vector

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

     Henning Redestig

_R_e_f_e_r_e_n_c_e_s:

     Introduction to Mult- and Megavaraite Data Analysis uing
     Projection Methods (PCA and PLS), L. Eriksson, E. Johansson, N.
     Kettaneh-Wold and S. Wold, Umetrics 1999, p. 466

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

     data(iris)
     pcIr <- pca(iris[,1:4])
     ## versicolor has the lowest leverage
     plot(leverage(pcIr)~iris$Species)

