labelParams             package:Envisage             R Documentation

_R_e_m_o_v_e _l_a_b_e_l_l_i_n_g _v_a_r_i_a_b_l_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function removes any categorical variables which have the
     same number of levels as there are samples (labelling variables).

_U_s_a_g_e:

       labelParams(pData) 

_A_r_g_u_m_e_n_t_s:

   pData: An object of class 'phenoData' containing the phenotypic
          information for the experiment.

_D_e_t_a_i_l_s:

     The 'Envisage' package contains methods allowing the use of linear
     models (LMs) for analysing significantly changing genes in
     experiments with a variety of sources of variation, be they
     experimentally controlled variables such as drug treatment or
     time, or non-controlled   sources of confounding variation such as
     phenotypic or environmental  differences. This allows all sources
     of variation to be considered when  analysing for significant
     differential expression, ensuring resulting genes are biologically
     relevent to the experimental question.

     The function 'labelParams' ensures that any categorical variables
     included by the user for model fitting that have the same number
     of factor levels as there are samples are removed prior to fitting
     the model. Such labelling variables contain a unique value for
     each sample, and will this result in a perfect fit for the data. 

     'labelParams' is an internal function and is not designed to be
     accessed by users.

_V_a_l_u_e:

     A vector is returned containing the indices of the labelling
     parameters for the 'phenoData' object 'pData'.

_A_u_t_h_o_r(_s):

     Sam Robson S.C.Robson@warwick.ac.uk

_R_e_f_e_r_e_n_c_e_s:

     Robson, S. C., Hunter, E., Bird, H., Turner, H. (2008)  _Envisage:
     model-based significance analysis of microarray gene expression
     data_, manuscript in preparation

_S_e_e _A_l_s_o:

     For the main 'Envisage' method, see 'envisage'.

