addint                  package:qtl                  R Documentation

_A_d_d _p_a_i_r_w_i_s_e _i_n_t_e_r_a_c_t_i_o_n _t_o _a _m_u_l_t_i_p_l_e-_Q_T_L _m_o_d_e_l

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

     Try adding all possible pairwise interactions, one at a time, to a
     multiple QTL model.

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

     addint(cross, pheno.col=1, qtl, covar=NULL, formula, method=c("imp","hk"),
            qtl.only=FALSE, verbose=TRUE)

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

   cross: An object of class 'cross'. See 'read.cross' for details.

pheno.col: Column number in the phenotype matrix which should be used
          as the phenotype.  One may also give a character string
          matching a phenotype name.

     qtl: An object of class 'qtl', as output from 'makeqtl'.

   covar: A data.frame of covariates.  These must be strictly numeric.

 formula: An object of class 'formula' indicating the model to be
          fitted.  QTLs are referred to as 'Q1', 'Q2', etc.  Covariates
          are referred to by their names in the data frame 'covar'.

  method: Indicates whether to use multiple imputation or Haley-Knott
          regression.

qtl.only: If TRUE, only test QTL:QTL interactions (and not interactions
          with covariates).

 verbose: If TRUE, will print a message if there are no interactions to
          test.

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

     The formula is used to specified the model to be fit. In the
     formula, use 'Q1', 'Q2', etc., or 'q1', 'q2', etc., to represent
     the QTLs, and the column names in the covariate data frame to
     represent the covariates.

     We enforce a hierarchical structure on the model formula: if a QTL
     or covariate is in involved in an interaction, its main effect
     must also be included.

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

     An object of class 'addint', with results as in the drop-one-term
     analysis from 'fitqtl'.  This is a data frame (given class
     '"addint"', with the following columns:  degrees of freedom (df),
     Type III sum of squares (Type III SS), LOD score(LOD), percentage
     of variance explained (%var), F statistics (F value),  and P
     values for chi square (Pvalue(chi2)) and F distribution
     (Pvalue(F)).

     Note that the degree of freedom, Type III sum of squares, the LOD
     score and the percentage of variance explained are the values
     comparing the full to the sub-model with the term dropped. Also
     note that for imputation method, the percentage of variance
     explained, the the F values and the P values are approximations
     calculated from the LOD score.

     Pairwise interactions already included in the input 'formula' are
     not tested.

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

     Karl W Broman, kbroman@biostat.wisc.edu

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

     Haley, C. S. and Knott, S. A. (1992) A simple regression method
     for mapping quantitative trait loci in line crosses using flanking
     markers. _Heredity_ *69*, 315-324.

     Sen, \'S. and Churchill, G. A. (2001) A statistical framework for
     quantitative trait mapping.  _Genetics_ *159*, 371-387.

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

     'fitqtl', 'makeqtl', 'scanqtl', 'refineqtl', 'addqtl', 'addpair'

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

     data(fake.f2)

     # take out several QTLs and make QTL object
     qc <- c(1, 8, 13)
     qp <- c(26, 56, 28)
     fake.f2 <- subset(fake.f2, chr=qc)

     fake.f2 <- sim.geno(fake.f2, n.draws=8, step=2, err=0.001)
     qtl <- makeqtl(fake.f2, qc, qp)

     # try all possible pairwise interactions, one at a time
     addint(fake.f2, pheno.col=1, qtl, formula=y~Q1+Q2+Q3)

