pdmClass              package:pdmclass              R Documentation

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_D_i_s_c_r_i_m_i_n_a_n_t _M_e_t_h_o_d_s

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

     This function is used to classify microarray data. Since the
     underlying model fit is based on penalized discriminant methods,
     there is no need for a pre-filtering step to reduce the number of
     genes.

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

     pdmClass(formula , method = c("pls", "pcr", "ridge"), ...)

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

 formula: A symbolic description of the model to be fit. Details given
          below. 

  method: One of "pls", "pcr", "ridge", corresponding to partial least
          squares, principal components regression and ridge
          regression.

     ...: Additional parameters to pass to 'method' or 'fda'. See 'fda'
          for more information.

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

     The formula interface is identical to all other formula calls in
     R, namely Y ~ X, where Y is a numeric vector of class assignments
     and X is a matrix or data.frame containing the gene expression
     values. Note that unlike most microarray analyses, in this
     instance the columns of X are genes and rows are samples, so most
     calls will require something similar to Y ~ t(X).

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

     an object of class '"fda"'.  Use 'predict' to extract discriminant
     variables, posterior probabilities or predicted class memberships.
      Other extractor functions are 'coef', and 'plot'.

     The object has the following components: 

percent.explained: the percent between-group variance explained by each
          dimension (relative to the total explained.)

  values: optimal scaling regresssion sum-of-squares for each dimension
          (see reference).  The usual discriminant analysis eigenvalues
          are given by 'values / (1-values)', which are used to define
          'percent.explained'.

   means: class means in the discriminant space.  These are also scaled
          versions of the final theta's or class scores, and can be
          used in a subsequent call to 'fda' (this only makes sense if
          some columns of theta are omitted-see the references).

theta.mod: (internal) a class scoring matrix which allows 'predict' to
          work properly.

dimension: dimension of discriminant space.

   prior: class proportions for the training data.

     fit: fit object returned by 'method'.

    call: the call that created this object (allowing it to be
          'update'-able)

confusion: A 'confusion' matrix that shows how well the classifier
          works using the training data.

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

     James W. MacDonald and Debashis Ghosh, based on 'fda' in the 'mda'
     package of Trevor Hastie and Robert Tibshirani, which was ported
     to R by Kurt Hornik, Brian D. Ripley, and Friedrich Leisch.

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

     http://www.sph.umich.edu/~ghoshd/COMPBIO/POPTSCORE

     "Flexible Disriminant Analysis by Optimal Scoring"  by Hastie,
     Tibshirani and Buja, 1994, JASA, 1255-1270.

     "Penalized Discriminant Analysis" by Hastie, Buja and Tibshirani,
     Annals of Statistics, 1995 (in press).

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

     library(fibroEset)
     data(fibroEset)
     y <- as.factor(pData(fibroEset)[,2])
     x <- t(exprs(fibroEset))
     pdmClass(y ~ x)

