pca            package:pcaMethods            R Documentation(latin1)

_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

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

     Can be used for computing PCA on a numeric matrix for
     visualisation, information extraction and missing value
     imputation.

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

     pca(object, method=c("svd", "nipals", "bpca", "ppca",
     "svdImpute", "nlpca", "robustPca"), subset=numeric(),...)

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

  object: Numerical matrix  with (or an object coercible to such) with
          samples in rows and variables as columns. Also takes
          'exprSet' in which case the transposed 'exprs' slot is used.

  subset: For convenience one can pass a large matrix but only use the
          variable specified as subset. Can be colnames or indices.

  method: One of "svd", "nipals", "bpca", "nlpca" or "ppca".

     ...: Further arguments to the chosen pca method.

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

     This method is wrapper function for the following set of pca
     methods:

     _s_v_d: Uses classical 'prcomp'. See documentation for 'svdPca'.

     _n_i_p_a_l_s: An iterative method capable of handling small amounts of
          missing values. See documentation for 'nipalsPca'.

     _b_p_c_a: An iterative method using a Bayesian model to handle missing
          values. See documentation for 'bpca'.

     _p_p_c_a: An iterative method using a probabilistic model to handle
          missing values. See documentation for 'ppca'.

     _s_v_d_I_m_p_u_t_e: Uses expectation maximation to perform SVD PCA on
          incomplete data. See documentation for 'svdImpute'.

     Extra arguments usually given to this function include:

     _n_P_c_s: The amount of principal components to extract

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

     A 'pcaRes' object. Or a list containing a pcaRes object as first
     and an exprSet object as second entry if the input was of type
     exprSet.

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

     Wolfram Stacklies, 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.

     Shigeyuki Oba, Masa-aki Sato, Ichiro Takemasa, Morito Monden,
     Ken-ichi Matsubara and Shin Ishii. A Bayesian missing value
     estimation method for gene expression profile data.
     _Bioinformatics, 19(16):2088-2096, Nov 2003_.

     Troyanskaya O. and Cantor M. and Sherlock G. and Brown P. and
     Hastie T. and Tibshirani R. and Botstein D. and Altman RB. -
     Missing value estimation methods for DNA microarrays.
     _Bioinformatics. 2001 Jun;17(6):520-5_.

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

     'prcomp', 'princomp', 'nipalsPca', 'svdPca'

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

     data(iris)
     ## Usually some kind of scaling is appropriate
     pcIr <- pca(iris[,1:4], nPcs = 2, method="nipals")
     pcIr <- pca(iris[,1:4], nPcs = 2, method="svd")
     ## Get a short summary on the calculated model
     summary(pcIr)
     ## Scores and loadings plot
     slplot(pcIr, sl=as.character(iris[,5]))

