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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: Plot LOD*Effect curves of 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 mqmplot.directedqtl {qtl}"><tr><td>mqmplot.directedqtl {qtl}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2>Plot LOD*Effect curves of a multiple-QTL model </h2> <h3>Description</h3> <p>Plot the LOD*Effect curve for a genome scan with a multiple-QTL model (the output of <code><a href="mqmscan.html">mqmscan</a></code>). </p> <h3>Usage</h3> <pre> mqmplot.directedqtl(cross, mqmresult, pheno.col=1, draw = TRUE) </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>mqmresult</code></td> <td> <p> Results from mqmscan of type <code>scanone</code> </p> </td></tr> <tr valign="top"><td><code>pheno.col</code></td> <td> <p> From which phenotype in the crossobject are the result calculated </p> </td></tr> <tr valign="top"><td><code>draw</code></td> <td> <p> If TRUE, draw the figure. </p> </td></tr> </table> <h3>Value</h3> <p>Returns a scanone object, with added the effectsign calculated internally by the function effect.scan. For more info on the scanone object see: <code><a href="scanone.html">scanone</a></code> </p> <h3>Author(s)</h3> <p>Danny Arends <a href="mailto:danny.arends@gmail.com">danny.arends@gmail.com</a> </p> <h3>See Also</h3> <ul> <li><p> The MQM tutorial: <a href="https://rqtl.org/tutorials/MQM-tour.pdf">https://rqtl.org/tutorials/MQM-tour.pdf</a> </p> </li> <li> <p><code><a href="MQM.html">MQM</a></code> - MQM description and references </p> </li> <li> <p><code><a href="mqmscan.html">mqmscan</a></code> - Main MQM single trait analysis </p> </li> <li> <p><code><a href="mqmscanall.html">mqmscanall</a></code> - Parallellized traits analysis </p> </li> <li> <p><code><a href="mqmaugment.html">mqmaugment</a></code> - Augmentation routine for estimating missing data </p> </li> <li> <p><code><a href="mqmautocofactors.html">mqmautocofactors</a></code> - Set cofactors using marker density </p> </li> <li> <p><code><a href="mqmsetcofactors.html">mqmsetcofactors</a></code> - Set cofactors at fixed locations </p> </li> <li> <p><code><a href="mqmpermutation.html">mqmpermutation</a></code> - Estimate significance levels </p> </li> <li> <p><code><a href="scanone.html">scanone</a></code> - Single QTL scanning </p> </li></ul> <h3>Examples</h3> <pre> #Simulated F2 Population f2qtl <- c(3,15,1,0) # QTL at chromosome 3 data(map10) # Mouse genetic map f2cross <- sim.cross(map10,f2qtl,n=100,type="f2") # Simulate a F2 Cross f2cross <- fill.geno(f2cross) # Fill in missing genotypes f2result <- mqmscan(f2cross) # Do a MQM scan of the genome mqmplot.directedqtl(f2cross,f2result) </pre> <hr /><div style="text-align: center;">[Package <em>qtl</em> version 1.46-2 <a href="00Index.html">Index</a>]</div> </body></html>