corParams              package:Envisage              R Documentation

_F_l_a_g _h_i_g_h_l_y _c_o_r_r_e_l_a_t_e_d _p_a_r_a_m_e_t_e_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     Flags all highly correlated variables which model the same data to
     reduce the number of variables and interactions in the model
     calculation.

_U_s_a_g_e:

       corParams(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 'corParams' identifies variables within the study
     design that are highly correlated and flags them up for the user.
     These may represent different representations of the same data and
     will affect model fitting, so should be removed from the data set.
     Variables found to be highly correlated are suffixed with the
     flags '*', '**', '***' etc.

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

_V_a_l_u_e:

     A list is returned containing the following slots:

corParamNames: A character vector of the names of the parameters found
          to have very high correlation, suffixed with flags '*', '**',
          '***' etc.

corParams: A list containing the indices for variables found to be
          highly correlated with one another.

_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'.

