| mqmplot.clusteredheatmap | R Documentation | 
Plot the results from a MQM scan on multiple phenotypes.
mqmplot.clusteredheatmap(cross, mqmresult, directed=TRUE, legend=FALSE,
                         Colv=NA, scale="none", verbose=FALSE,
                         breaks = c(-100,-10,-3,0,3,10,100),
                         col = c("darkblue","blue","lightblue","yellow",
                                 "orange","red"), ...)
| cross | An object of class  | 
| mqmresult |  Result object from mqmscanall, the object needs to be of class  | 
| directed | Take direction of QTLs into account (takes more time because of QTL direction calculations | 
| legend | If TRUE, add a legend to the plot | 
| Colv | Cluster only the Rows, the columns (Markers) should not be clustered | 
| scale | character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. The default "none" | 
| verbose | If TRUE, give verbose output. | 
| breaks | Color break points for the LOD scores | 
| col | Colors used between breaks | 
| ... | Additional arguments passed to  | 
Danny Arends danny.arends@gmail.com
The MQM tutorial: https://rqtl.org/tutorials/MQM-tour.pdf
MQM - MQM description and references
mqmscan - Main MQM single trait analysis
mqmscanall - Parallellized traits analysis
mqmaugment - Augmentation routine for estimating missing data
mqmautocofactors - Set cofactors using marker density
mqmsetcofactors - Set cofactors at fixed locations
mqmpermutation - Estimate significance levels
scanone - Single QTL scanning
data(multitrait)
multitrait <- fill.geno(multitrait) # impute missing genotype data
result <- mqmscanall(multitrait, logtransform=TRUE)
cresults <- mqmplot.clusteredheatmap(multitrait,result)
groupclusteredheatmap(multitrait,cresults,10)
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