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, pvalues=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 to be used as the
          phenotype.  One may also give a character string matching a
          phenotype name. Finally, one may give a numeric vector of
          phenotypes, in which case it must have the length equal to
          the number of individuals in the cross, and there must be
          either non-integers or values < 1 or > no. phenotypes; this
          last case may be useful for studying transformations.

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

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

 formula: An object of class 'formula' indicating the model to be
          fitted.  (It can also be the character string representation
          of a formula.)  QTLs are referred to as 'Q1', 'Q2', etc. 
          Covariates are referred to by their names in the data frame
          'covar'.  If the new QTL is not included in the formula, its
          main effect is added.

  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.

 pvalues: If FALSE, p-values will not be included in the results.

_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:

     'addcovarint', '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 <- calc.genoprob(fake.f2, step=2, err=0.001)
     qtl <- makeqtl(fake.f2, qc, qp, what="prob")

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

