PlotPedigree {CoSeg} | R Documentation |
This function uses the kinship2 package for easily plotting a tree.
PlotPedigree(ped, affected.vector=NULL, legend.location="topleft", legend.radius=0.1)
ped |
A tree generated from |
affected.vector |
A vector indicating which individuals are afffected(1). Note that this does not differentiate unaffected(0) and unknown affection status(2). |
legend.location |
A string indicating the placement of the legend. These can be "topleft", "topright", "bottomleft", "bottomright". |
legend.radius |
A real number indicating the size of the legend. |
No values returned but a plot is displayed.
John Michael O. Ranola and Brian H. Shirts
## Not run: #Load all the data included in the CoSeg package. data(BRCA1Frequencies.df, package="CoSeg") data(BRCA2Frequencies.df, package="CoSeg") data(MLH1Frequencies.df, package="CoSeg") data(USDemographics.df, package="CoSeg") data(ChinaDemographics.df, package="CoSeg") #summaries of all the data str(BRCA1Frequencies.df) str(BRCA2Frequencies.df) str(MLH1Frequencies.df) str(USDemographics.df) str(ChinaDemographics.df) #Make a tree with no affection status, g=4 generations above, gdown=2 generations below, #seed.age=50, and demographics.df=NULL which defaults to USDemographics.df. tree1=MakeTree() #Make a tree using Chinese demographics instead. tree2=MakeTree(demographics.df=ChinaDemographics.df) #Add affection statust to tree2 using BRCA1Frequencies.df which gives the BRCA1 #penetrance function tree1a=AddAffectedToTree(tree.f=tree1,frequencies.df=BRCA1Frequencies.df) #make a tree with affection status (same as running MakeTree() and then AddAffectedToTree()) tree3=MakeAffectedTrees(n=1,g=2,gdown=2,frequencies.df=MLH1Frequencies.df) #tree4=MakeAffectedTrees(n=1,g=2,gdown=2,frequencies.df=BRCA2Frequencies.df) #Depending on the size of the pedigree generated, probands (defined here as members of the #pedigree who are carriers of the genotype with the disease) may not always be present in #the pedigree. To alleviate this problem in this example we manually generate a pedigree. #Note that this is from the Mohammadi paper where the Likelihood method originates from. ped=data.frame(degree=c(3,2,2,3,3,1,1,2,2,3), momid=c(3,NA,7,3,3,NA,NA,7,NA,8), dadid=c(2,NA,6,2,2,NA,NA,6,NA,9), id=1:10, age=c(45,60,50,31,41,68,65,55,62,43), female=c(1,0,1,0,1,0,1,1,0,1), y.born=0, dead=0, geno=2, famid=1, bBRCA1.d=0, oBRCA1.d=0, bBRCA1.aoo=NA, oBRCA1.aoo=NA, proband=0) ped$y.born=2010-ped$age ped$geno[c(1,3)]=1 ped$bBRCA1.d[c(1,3)]=1 ped$bBRCA1.aoo[1]=45 ped$bBRCA1.aoo[3]=50 ped$proband[1]=1 ped=ped[c(6,7,2,3,8,9,1,4,5,10),] #Calculate the likelihood ratio CalculateLikelihoodRatio(ped=ped, affected.vector={ped$bBRCA1.d|ped$oBRCA1.d}, gene="BRCA1") #Plot the pedigree PlotPedigree(ped, affected.vector={ped$bBRCA1.d|ped$oBRCA1.d}) #Rank and plot the members of the pedigree with unknown genotypes RankMembers(ped=ped, affected.vector={ped$bBRCA1.d|ped$oBRCA1.d}, gene="BRCA1") ## End(Not run)