INTlevels              package:Envisage              R Documentation

_C_a_l_c_u_l_a_t_e _t_h_e _t_o_t_a_l _n_u_m_b_e_r _o_f _l_e_v_e_l_s _w_i_t_h_i_n _a _m_o_d_e_l _f_o_r _p_a_c_k_a_g_e
_E_n_v_i_s_a_g_e

_D_e_s_c_r_i_p_t_i_o_n:

     This function calculates the total number of levels of all
     variables and interactions within the maximal model considered in
     the stepwise modelling procedure of 'Envisage'.

_U_s_a_g_e:

       INTlevels(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 'INTlevels' calculates the total number of levels of
     all main effect and interaction terms in the maximal model
     considered within the stepwise modelling procedure of 'Envisage'.
     If too many variables are included in the model, with a greater
     total of levels than there are data points, this may result in
     incorrect fitting of the model. This should be avoided by being
     aware of how many degrees of freedom each variable that you want
     to include will take up. If a variable of interest has a large
     number of possible levels, consider combining some of these to
     avoid over-fitting the data or only use this as a main effect term
     instead of considering all  interactions. 

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

_V_a_l_u_e:

     A vector is returned containing the total number of levels
     required for each main effect and first order interation of
     variables within the analysis.

_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'.

