Description Usage Format Source Examples
A dataframe containing average age of marriage, death, and number offspring by year for individuals in China.
1 |
A data frame with 12 observations on the following 7 variables.
in.year
A numeric vector giving the input year.
out.year
A numeric vector giving the output year.
female.age.marriage
A numeric vector giving the average age of marriage for females by year.
male.age.marriage
A numeric vector giving the average age of marriage for males by year.
female.age.death
A numeric vector giving the average age of death for females by year.
male.age.death
A numeric vector giving the average age of death for males by year.
offspring
A numeric vector giving the average numbers of offspring for families by year.
Data were taken from the following sources. http://www.digitalhistory.uh.edu/ http://demog.berkeley.edu/~andrew/1918/figure2.html http://www.infoplease.com/ipa/A0005148.html http://www.infoplease.com/ipa/A0005061.html http://www.gapminder.org
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | ## 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)
|
Loading required package: kinship2
Loading required package: Matrix
Loading required package: quadprog
Loading required package: fGarch
Loading required package: timeDate
Loading required package: timeSeries
Loading required package: fBasics
Rmetrics Package fBasics
Analysing Markets and calculating Basic Statistics
Copyright (C) 2005-2014 Rmetrics Association Zurich
Educational Software for Financial Engineering and Computational Science
Rmetrics is free software and comes with ABSOLUTELY NO WARRANTY.
https://www.rmetrics.org --- Mail to: info@rmetrics.org
Loading required package: splines
Warning message:
In data(BRCA1Frequencies.df, package = "CoSeg") :
data set 'BRCA1Frequencies.df' not found
Warning message:
In data(BRCA2Frequencies.df, package = "CoSeg") :
data set 'BRCA2Frequencies.df' not found
Warning message:
In data(MLH1Frequencies.df, package = "CoSeg") :
data set 'MLH1Frequencies.df' not found
Warning message:
In data(USDemographics.df, package = "CoSeg") :
data set 'USDemographics.df' not found
Warning message:
In data(ChinaDemographics.df, package = "CoSeg") :
data set 'ChinaDemographics.df' not found
'data.frame': 800 obs. of 5 variables:
$ age : int 1 2 3 4 5 6 7 8 9 10 ...
$ cancer.type: Factor w/ 2 levels "bBRCA1","oBRCA1": 1 1 1 1 1 1 1 1 1 1 ...
$ female : num 0 0 0 0 0 0 0 0 0 0 ...
$ carrier : num 0 0 0 0 0 0 0 0 0 0 ...
$ frequencies: num 0 0 0 0 0 0 0 0 0 0 ...
'data.frame': 800 obs. of 5 variables:
$ age : int 1 2 3 4 5 6 7 8 9 10 ...
$ cancer.type: Factor w/ 2 levels "bBRCA2","oBRCA2": 1 1 1 1 1 1 1 1 1 1 ...
$ female : num 0 0 0 0 0 0 0 0 0 0 ...
$ carrier : num 0 0 0 0 0 0 0 0 0 0 ...
$ frequencies: num 0 0 0 0 0 0 0 0 0 0 ...
'data.frame': 1200 obs. of 5 variables:
$ age : int 1 2 3 4 5 6 7 8 9 10 ...
$ cancer.type: Factor w/ 3 levels "crcLS","endLS",..: 1 1 1 1 1 1 1 1 1 1 ...
$ female : num 0 0 0 0 0 0 0 0 0 0 ...
$ carrier : num 0 0 0 0 0 0 0 0 0 0 ...
$ frequencies: num 0 0 0 0 0 0 0 0 0 0 ...
'data.frame': 12 obs. of 7 variables:
$ in.year : num 1800 1900 1910 1920 1930 1940 1950 1960 1970 1980 ...
$ out.year : num 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 ...
$ female.age.marriage: num 22 21.1 21.6 21.2 21.3 ...
$ male.age.marriage : num 26.1 26.1 25.1 24.6 24.3 24.3 22.8 23.2 24.7 26.1 ...
$ female.age.death : num 60.2 62 63.8 64.9 66.5 ...
$ male.age.death : num 60.1 60.7 62.2 62.7 65.6 ...
$ offspring : num 3.15 2.93 2.88 2.74 2.21 ...
'data.frame': 12 obs. of 7 variables:
$ in.year : num 1800 1900 1910 1920 1930 1940 1950 1960 1970 1980 ...
$ out.year : num 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 ...
$ female.age.marriage: num 17.5 17.5 17.5 17.5 17.5 18.5 19 19.8 21.6 22.8 ...
$ male.age.marriage : num 20.8 22.2 22.2 22.2 20.5 23.6 24 24.4 24.8 25.2 ...
$ female.age.death : num 53.6 55.7 57.8 60.1 60.1 ...
$ male.age.death : num 57.5 58.1 58.7 59.3 60.7 ...
$ offspring : num 3.19 3.24 3.3 3.35 3.35 ...
[1] "No demographics given. Using USDemographics.df"
[1] 0.01
[1] 0.02
[1] 0.03
[1] 0.04
[1] 0.05
[1] 0.06
[1] 0.07
[1] 0.08
[1] 0.09
[1] 0.1
[1] 0.11
[1] 0.12
[1] 0.13
[1] 0.14
[1] 0.15
[1] 0.16
[1] 0.17
[1] 0.18
[1] 0.19
[1] 0.2
[1] 0.21
[1] 0.22
[1] 0.23
[1] 0.24
[1] 0.25
[1] 0.26
[1] 0.27
[1] 0.28
[1] 0.29
[1] 0.3
[1] 0.31
[1] 0.32
[1] 0.33
[1] 0.34
[1] 0.35
[1] 0.36
[1] 0.37
[1] 0.38
[1] 0.39
[1] 0.4
[1] 0.41
[1] 0.42
[1] 0.43
[1] 0.44
[1] 0.45
[1] 0.46
[1] 0.47
[1] 0.48
[1] 0.49
[1] 0.5
[1] 0.51
[1] 0.52
[1] 0.53
[1] 0.54
[1] 0.55
[1] 0.56
[1] 0.57
[1] 0.58
[1] 0.59
[1] 0.6
[1] 0.61
[1] 0.62
[1] 0.63
[1] 0.64
[1] 0.65
[1] 0.66
[1] 0.67
[1] 0.68
[1] 0.69
[1] 0.7
[1] 0.71
[1] 0.72
[1] 0.73
[1] 0.74
[1] 0.75
[1] 0.76
[1] 0.77
[1] 0.78
[1] 0.79
[1] 0.8
[1] 0.81
[1] 0.82
[1] 0.83
[1] 0.84
[1] 0.85
[1] 0.86
[1] 0.87
[1] 0.88
[1] 0.89
[1] 0.9
[1] 0.91
[1] 0.92
[1] 0.93
[1] 0.94
[1] 0.95
[1] 0.96
[1] 0.97
[1] 0.98
[1] 0.99
[1] 1
[1] "No demographics given. Using USDemographics.df"
[1] 1
[1] 0.04
[1] 0.08
[1] 0.12
[1] 0.15
[1] 0.19
[1] 0.23
[1] 0.27
[1] 0.31
[1] 0.35
[1] 0.38
[1] 0.42
[1] 0.46
[1] 0.5
[1] 0.54
[1] 0.58
[1] 0.62
[1] 0.65
[1] 0.69
[1] 0.73
[1] 0.77
[1] 0.81
[1] 0.85
[1] 0.88
[1] 0.92
[1] 0.96
[1] 1
$likelihood.ratio
[1] 1.704142
$separating.meioses
[1] 1
$number.genotypes.found
[1] 28
$unknown.genotypes
[1] 6 7 8 4 5 10
$modified.lr
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.8803743 2.5279092 2.301785 1.704213 1.862173 1.7673232
[2,] 2.5279092 0.8803743 1.106498 1.704071 1.546111 0.9801351
$original.lr
[1] 1.704142
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