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 = formula(data), method = c("pls", "pcr", "ridge"),
     data = sys.frame(sys.parent()), weights, theta, dimension = J - 1,
     eps = .Machine$double.eps, ...)

_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.

    data: An optional data.frame that contains the variables in the
          model. If not found in 'data', the variables are taken from
          'environment(formula)', typically the environment from which
          'pdmClass' is called. Note that unlike most microarray
          analyses, in this case rows are samples and columns are
          genes.

 weights: An optional vector of sample weights. Defaults to 1. 

   theta: An optional matrix of class scores, typically with less than
          J - 1 columns.

dimension: The dimension of the solution. This will be no greater than
          J - 1 for partial least squares and ridge regression, and no
          greater than J for principal components regression. Defaults
          to J - 1 and J, respectively. 

     eps: A threshold for excluding small discriminant variables.
          Defaults to '.Machine$double.eps'.

     ...: Additional parameters to pass to 'method'. 

_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)

_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)

