modelCalcINT            package:Envisage            R Documentation

_F_i_t _a _m_o_d_e_l _w_i_t_h _m_a_i_n _e_f_f_e_c_t _a_n_d _f_i_r_s_t _o_r_d_e_r _i_n_t_e_r_a_c_t_i_o_n 
_t_e_r_m_s _f_o_r _e_a_c_h _g_e_n_e _a_n_d _c_a_l_c_u_l_a_t_e _s_i_g_n_i_f_i_c_a_n_c_e

_D_e_s_c_r_i_p_t_i_o_n:

     This is the main calculation for the package 'Envisage'. An
     optimised model, containing terms shown to have practical
     importance for explaining variation, is produced using a step-wise
     procedure. Terms may be main effect terms or first order
     interactions. The significance of each term is computed by using a
     type II sum of squares.

_U_s_a_g_e:

       modelCalcINT(paramData, exprData, param4INT, startingModel,
         progress) 

_A_r_g_u_m_e_n_t_s:

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

exprData: A matrix of expression values for each gene for each sample.
          Usually calculated using 'exprs' on an  object of class
          'ExpressionSet'.

param4INT: A character vector specifying the variables for which first
          order interactions should be calculated.

startingModel: An object of class 'formula' containing the minimal
          model thta should be used in the step-wise model fitting
          process.

progress: Object of class 'tclVar' indicating progress through the
          genes in the gene list. Used only if 'widget=TRUE' for
          'envisage'.

_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 'modelCalcINT' performs the main modelling step for
     the package 'Envisage'. A stepwise procedure is used on a per-gene
     basis to calculate the optimum model for each gene. In comparison
     to 'modelCalcME', terms used in the model may be main effect terms
     or first-order interactions. The step-wise process begins by
     fitting the  maximal model to the data, then removes terms using a
     leave-one-out approach based on the Akaike information criterion
     (AIC). Specification of a minimal model allows terms to be
     'forced' into the final formula.

     To increase speed, the model fitting procedure is first performed
     using only main effect terms, and first order interactions are
     only calculated if the main effect term is thus shown to be
     significant.

     The significance of each term in the final model is estimated by
     using a Type II sum of squares to compare the model with and
     without each term. Also, if fitting the model results in aliased
     terms (either due to experimental design, insufficient data, or
     non-independence of model terms), this information is also
     returned.

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

_V_a_l_u_e:

     A list is returned with the following slots:

 pValues: An object of class 'data.frame' containing the p-value from
          the Type II sum of squares analysis for each model term  for
          each gene.

failedAnova: A vector of genes for which the Anova Type II sum of
          squares failed, indicating model aliasing.

errorGenes: A list of the genes that failed and the model terms for
          which aliasing was detected.

_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 effect only version of this function, see
     'modelCalcME'. For the main 'Envisage' method, see 'envisage'.

