CoSeg (version 0.38)

MakeTree: Make a tree

Description

This function makes a tree based on historical average age of marriage for male and female, age of death for both sexes, and number of offspring. For convenience, historical data for the US and China are included in the package.

Usage

MakeTree(g = 4, gdown = 2, seed.age = 50, demographics.df = NULL)

Arguments

g

An integer giving the number of ancestral generations of the proband to simulate.

gdown

An integer giving the number of descendent generations of the proband to simulate.

seed.age

An integer giving the suggested age of the proband.

demographics.df

A dataframe giving the demographics to use. This defaults to USDemographics.df which gives historic data on US demographics. Alternatively, the user can choose ChinaDemographics.df or input their own dataframe.

Value

Returns a dataframe for the generated tree. The dataframe has the columns listed below.

See Also

See also AddAffectedToTree, MakeAffectedTrees, and PlotPedigree

Examples

  ## Not run: 
# 
#     #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)