addcovarint               package:qtl               R Documentation

_A_d_d _Q_T_L _x _c_o_v_a_r_i_a_t_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 QTL x covariate interactions, one at a time, to a
     multiple QTL model, for a given set of covariates.

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

     addcovarint(cross, pheno.col=1, qtl, covar=NULL, icovar, formula, 
            method=c("imp","hk"), 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 which should 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.

  icovar: Vector of character strings indicating the columns in 'covar'
          to be considered for QTL x covariate interactions.

 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'.

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

 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 'addcovarint', with results as in the
     drop-one-term analysis from 'fitqtl'.  This is a data frame (given
     class '"addcovarint"', 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.

     QTL x covariate 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:

     'addint', '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")

     # use the sex phenotype as the covariate
     covar <- data.frame(sex=fake.f2$pheno$sex)

     # try all possible QTL x sex interactions, one at a time
     addcovarint(fake.f2, pheno.col=1, qtl, covar, "sex", y~Q1+Q2+Q3,
                 method="hk")

