rowaov                package:LMGene                R Documentation

_G_e_n_e _b_y _g_e_n_e _a_n_o_v_a _f_u_n_c_t_i_o_i_n

_D_e_s_c_r_i_p_t_i_o_n:

     Computes the mean squares and degrees of freedom for gene-by-gene
     anovas.

_U_s_a_g_e:

     rowaov(eS, model=NULL)

_A_r_g_u_m_e_n_t_s:

      eS: Array data. must be a exprSet type and the log-transformation
          and the normalization of exprSet@exprs are recommended 

   model: Model used for comparison. See details and 'LMGene'.

_D_e_t_a_i_l_s:

     The input argument, eS, must be exprSet type from Biobase package.
      If you have a matrix data and information about the considered
     factors, then you can use 'neweS' to conver the data into exprSet.
     Please see 'neweS' in more detail.

     'model' is an optional character string, constructed like the
     right-hand side of a formula for lm. It specifies which of the
     variables in the exprSet will be used in the model and whether
     interaction terms will be included. If model=NULL, it uses all
     variables from the exprSet without interactions. Be careful of
     using interaction terms with factors: this often leads to
     overfitting, which will yield an error.

_V_a_l_u_e:

 resmat : A matrix of MSE and DF of all factors for all genes

_A_u_t_h_o_r(_s):

     David Rocke and Geun-Cheol Lee

_R_e_f_e_r_e_n_c_e_s:

     David M. Rocke (2004), Design and analysis of experiments with
     high throughput biological assay data, Seminars in Cell &
     Developmental Biology, 15, 703-713. 

     <URL: http://www.idav.ucdavis.edu/~dmrocke/>

_S_e_e _A_l_s_o:

     'genediff', 'mlm2lm'

_E_x_a_m_p_l_e_s:

     #library
     library(Biobase)
     library(LMGene)

     #data
     data(sample.mat)
     data(vlist)
     LoggedSmpd0<-neweS(lnorm(log(sample.mat)),vlist)

     resmat <- rowaov(LoggedSmpd0)
     resmat[,1:3]

