princomp.rmult         package:compositions         R Documentation

_P_r_i_n_c_i_p_l_e _c_o_m_p_o_n_e_n_t _a_n_a_l_y_s_i_s _f_o_r _R_e_a_l _d_a_t_a

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

     Performes a principle component analysis for datasets of type
     rmult.

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

     ## S3 method for class 'rmult':
     princomp(x,...)

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

       x: a rmult-dataset

     ...: Further arguments to call of princomp.default

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

     The function just does 'princomp(unclass(x),...,scale=scale)' and
     is only here for convenience.

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

     An object of type 'princomp' with the following fields 

    sdev: the standard deviation of the principle components.

loadings: the matrix of variable loadings (i.e., a matrix whose columns
          contain the eigenvectors). This is of class '"loadings"'. The
          last eigenspace is removed since it should contain the
          irrelevant scaling.

  center: the clr of the means that was substracted

   scale: the scaling applied to each variable

   n.obs: number of observations

  scores: if 'scores = TRUE', the scores of the supplied data on the
          principle components and the information was available.
          Scores are coordinates in a basis given by the principle
          components and thus not compositions.

    call: the matched call

na.action: Not clearly understood

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

     'princomp.rplus'

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

     data(SimulatedAmounts)
     pc <- princomp(rmult(sa.lognormals5))
     pc
     summary(pc)
     plot(pc) 
     screeplot(pc)
     screeplot(pc,type="l")
     biplot(pc)
     biplot(pc,choice=c(1,3))
     loadings(pc)
     plot(loadings(pc))
     pc$sdev^2
     cov(predict(pc,sa.lognormals5))

