nipalsPca             package:pcaMethods             R Documentation

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_p_a_r_t_i_a_l _l_e_a_s_t _s_q_u_a_r_e_s (_N_I_P_A_L_S) _a_l_g_o_r_i_t_h_m.

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

     Can be used for computing PCA on a numeric matrix using either the
     NIPALS algorithm which is an iterative approach for estimating the
     principal components extracting them one at a time. NIPALS can
     handle a small amount of missing values.

     It is not recommended to use this function directely but rather to
     use the pca() wrapper function.

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

     nipalsPca(Matrix, nPcs=2, center=TRUE, completeObs=TRUE, varLimit=1, maxSteps=5000, 
       threshold=1e-6, verbose=interactive(),...)

_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: Mean center the data column wise if set TRUE

completeObs: Return the estimated complete observations. This is the
          input Matrix with NA values replaced by the estimated values.

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

maxSteps: Defines how many iterations can be done before the algorithm
          should abort (happens almost exclusively when there were some
          wrong in the input data).

threshold: The limit condition for judging if the algorithm has
          converged or not, specifically if a new iteration is done if
          (T_{old} - T)^T(T_{old} - T) > 'limit'.

 verbose: Show simple progress information.

     ...: Only used for passing through arguments.

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

     This method is quite slow what may lead to very long computation
     times when used on larger matrices. The power in missing value
     imputation is also quite disputable.

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

     A 'pcaRes' object.

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

     Henning Redestig

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

     Wold, H. (1966) Estimation of principal components and related
     models by iterative least squares. In Multivariate Analysis (Ed.,
     P.R. Krishnaiah), Academic Press, NY, 391-420.

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

     'prcomp', 'princomp', 'pca'

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

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

