| addint {qtl} | R Documentation |
Try adding all possible pairwise interactions, one at a time, to a multiple QTL model.
addint(cross, pheno.col=1, qtl, covar=NULL, formula, method=c("imp","hk"),
qtl.only=FALSE, verbose=TRUE, pvalues=TRUE)
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. |
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.
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.
Karl W Broman, kbroman@biostat.wisc.edu
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.
addcovarint, fitqtl, makeqtl,
scanqtl, refineqtl,
addqtl, addpair
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")