sla.princomp           package:RScaLAPACK           R Documentation

_P_r_i_n_c_i_p_a_l _C_o_m_p_o_n_e_n_t_s _A_n_a_l_y_s_i_s (_U_s_i_n_g _S_c_a_L_A_P_A_C_K)

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

     The function 'sla.prcomp' performs a principal components analysis
     on the give data matrix (using 'sla.eigen') and returns the
     results as a object of class 'princomp'.

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

     sla.princomp(x, ...)
     ## S3 method for class 'formula':
     sla.princomp(formula, data = NULL, subset, na.action, NPROWS=0, NPCOLS=0, MB=64, ...)
     ## Default S3 method:
     sla.princomp(x, cor = FALSE, scores = TRUE, covmat = NULL, subset = rep(TRUE, nrow(as.matrix(x))), NPROWS=0, NPCOLS=0, MB=64, ...)

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

 formula: a formula with no response variable.

    data: an optional data frame containing the variables in the
          formula 'formula'. By default the variables are taken from
          'environment(formula)'.

       x: a matrix or data frame which provides the data for the
          principal components analysis.

  subset: an optional vector used to select rows (observations) of the
          data matrix 'x'.

na.action: a function which indicates what should happen when the data
          contain 'NA's.  The default is set by the 'na.action' setting
          of 'options', and is 'na.fail' if that is unset. The
          "factory-fresh" default is 'na.omit'.

     cor: a logical value indicating whether the calculation should use
          the correlation matrix or the covariance matrix.

  scores: a logical value indicating whether the score on each
          principal component should be calculated.

  covmat: a covariance matrix, or a covariance list as returned by
          'cov.wt', 'cov.mve' or 'cov.mcd'. If supplied, this is used
          rather than the covariance matrix of 'x'.

     ...: arguments passed to or from other methods. If 'x' is a
          formula one might specify 'cor' or 'scores'.

  NPROWS: Number of Process Rows in the Process Grid. 

  NPCOLS: Number of Process Cols in the Process Grid. 

      MB: Block Size. 

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

     If the number of processor rows and columns are both zero, one
     processor is used.  If the number of processor rows is nonzero and
     the number of processor columns is zero, then the processor rows
     is taken to be a  number of processors, and a grid is made
     accordingly.

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

     'sla.princomp' returns a list with class '"princomp"' containing
     the following components: 

    sdev: the standard deviations of the principal components.

loadings: the matrix of variable loadings (i.e., a matrix whose columns
          contain the eigenvectors).  This is of class '"loadings"':
          see 'loadings' for its 'print' method.

  center: the means that were subtracted.

   scale: the scalings applied to each variable.

   n.obs: the number of observations.

  scores: if 'scores = TRUE', the scores of the supplied data on the
          principal components.

    call: the matched call.

na.action: If relevant.

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

     'summary.princomp', 'screeplot', 'biplot.princomp', 'prcomp',
     'cor', 'cov', 'eigen'.

     <URL:
     http://www.google.com/search?q=principal+components+analysis>

     <URL: http://www.netlib.org/scalapack/>

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

     'prcomp' for the normal PCA function.

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

     library(RScaLAPACK)
     data(USArrests)
     sla.princomp(USArrests, cor=TRUE)

