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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Add pairwise interaction to a multiple-QTL model</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for addint {qtl}"><tr><td>addint {qtl}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Add pairwise interaction to a multiple-QTL model</h2> <h3>Description</h3> <p>Try adding all possible pairwise interactions, one at a time, to a multiple QTL model. </p> <h3>Usage</h3> <pre> addint(cross, pheno.col=1, qtl, covar=NULL, formula, method=c("imp","hk"), model=c("normal", "binary"), qtl.only=FALSE, verbose=TRUE, pvalues=TRUE, simple=FALSE, tol=1e-4, maxit=1000, require.fullrank=FALSE) </pre> <h3>Arguments</h3> <table summary="R argblock"> <tr valign="top"><td><code>cross</code></td> <td> <p>An object of class <code>cross</code>. See <code><a href="read.cross.html">read.cross</a></code> for details.</p> </td></tr> <tr valign="top"><td><code>pheno.col</code></td> <td> <p>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.</p> </td></tr> <tr valign="top"><td><code>qtl</code></td> <td> <p>An object of class <code>qtl</code>, as output from <code><a href="makeqtl.html">makeqtl</a></code>.</p> </td></tr> <tr valign="top"><td><code>covar</code></td> <td> <p>A matrix or data.frame of covariates. These must be strictly numeric.</p> </td></tr> <tr valign="top"><td><code>formula</code></td> <td> <p>An object of class <code><a href="../../stats/html/formula.html">formula</a></code> indicating the model to be fitted. (It can also be the character string representation of a formula.) QTLs are referred to as <code>Q1</code>, <code>Q2</code>, etc. Covariates are referred to by their names in the data frame <code>covar</code>. If the new QTL is not included in the formula, its main effect is added.</p> </td></tr> <tr valign="top"><td><code>method</code></td> <td> <p>Indicates whether to use multiple imputation or Haley-Knott regression.</p> </td></tr> <tr valign="top"><td><code>model</code></td> <td> <p>The phenotype model: the usual model or a model for binary traits</p> </td></tr> <tr valign="top"><td><code>qtl.only</code></td> <td> <p>If TRUE, only test QTL:QTL interactions (and not interactions with covariates).</p> </td></tr> <tr valign="top"><td><code>verbose</code></td> <td> <p>If TRUE, will print a message if there are no interactions to test.</p> </td></tr> <tr valign="top"><td><code>pvalues</code></td> <td> <p>If FALSE, p-values will not be included in the results.</p> </td></tr> <tr valign="top"><td><code>simple</code></td> <td> <p>If TRUE, don't include p-values or sums of squares in the summary.</p> </td></tr> <tr valign="top"><td><code>tol</code></td> <td> <p>Tolerance for convergence for the binary trait model.</p> </td></tr> <tr valign="top"><td><code>maxit</code></td> <td> <p>Maximum number of iterations for fitting the binary trait model.</p> </td></tr> <tr valign="top"><td><code>require.fullrank</code></td> <td> <p>If TRUE, give LOD=0 when covariate matrix in the linear regression is not of full rank.</p> </td></tr> </table> <h3>Details</h3> <p>The formula is used to specified the model to be fit. In the formula, use <code>Q1</code>, <code>Q2</code>, etc., or <code>q1</code>, <code>q2</code>, etc., to represent the QTLs, and the column names in the covariate data frame to represent the covariates. </p> <p>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. </p> <h3>Value</h3> <p>An object of class <code>addint</code>, with results as in the drop-one-term analysis from <code><a href="fitqtl.html">fitqtl</a></code>. This is a data frame (given class <code>"addint"</code>, 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)). </p> <p>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. </p> <p>Pairwise interactions already included in the input <code>formula</code> are not tested. </p> <h3>Author(s)</h3> <p>Karl W Broman, <a href="mailto:broman@wisc.edu">broman@wisc.edu</a></p> <h3>References</h3> <p>Haley, C. S. and Knott, S. A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. <em>Heredity</em> <b>69</b>, 315–324. </p> <p>Sen, Ś. and Churchill, G. A. (2001) A statistical framework for quantitative trait mapping. <em>Genetics</em> <b>159</b>, 371–387. </p> <h3>See Also</h3> <p><code><a href="addcovarint.html">addcovarint</a></code>, <code><a href="fitqtl.html">fitqtl</a></code>, <code><a href="makeqtl.html">makeqtl</a></code>, <code><a href="scanqtl.html">scanqtl</a></code>, <code><a href="refineqtl.html">refineqtl</a></code>, <code><a href="addqtl.html">addqtl</a></code>, <code><a href="addpair.html">addpair</a></code> </p> <h3>Examples</h3> <pre> 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") </pre> <hr /><div style="text-align: center;">[Package <em>qtl</em> version 1.66 <a href="00Index.html">Index</a>]</div> </body></html>