svdPca              package:pcaMethods              R Documentation

_P_e_r_f_o_r_m _p_r_i_n_c_i_p_a_l _c_o_m_p_o_n_e_n_t _a_n_a_l_y_s_i_s _u_s_i_n_g _s_i_n_g_u_l_a_r _v_a_l_u_e _d_e_c_o_m_p_o_s_i_t_i_o_n

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

     A wrapper function for R's standard function 'prcomp'. Delivers
     the result as a 'pcaRes' method for compatibility with the rest of
     the pcaMethods package.

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

     svdPca(Matrix, nPcs=2, center=TRUE, completeObs=FALSE, varLimit=1,...)

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

  Matrix: Numerical matrix samples in rows and variables as columns.

    nPcs: Number of components that should be extracted.

  center: Center the data column wise if TRUE

completeObs: Return the complete observations. This exisits for
          compatibility only, as svdPca cannot missing values. If set
          TRUE the input matrix will be returned in the 'completeObs'
          field.

varLimit: Optionally the ratio of variance that should be explained.
          'nPcs' is ignored if varLimit < 1

     ...: Only used for passing through arguments.

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

     svdPca can preferrably be called using 'pca(object,
     method="svd")'.

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

     A 'pcaRes' object.

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

     Henning Redestig

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

     'prcomp', 'princomp', 'pca'

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

     data(iris)
     pcIr <- svdPca(iris[,1:4], nPcs=2)

