ChinaDemographics.df: Chinese Demographics Data Frame
In CoSeg: Cosegregation Analysis and Pedigree Simulation
Description
Usage
Format
Source
Examples
Description
A dataframe containing average age of marriage, death, and number offspring by year for individuals in China.
Usage
1
Format
A data frame with 12 observations on the following 7 variables.
in.yearA numeric vector giving the input year.
out.yearA numeric vector giving the output year.
female.age.marriageA numeric vector giving the average age of marriage for females by year.
male.age.marriageA numeric vector giving the average age of marriage for males by year.
female.age.deathA numeric vector giving the average age of death for females by year.
male.age.deathA numeric vector giving the average age of death for males by year.
offspringA numeric vector giving the average numbers of offspring for families by year.
Source
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
Examples
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)
Example output
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
CoSeg documentation built on May 29, 2017, 6:10 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Format Source Examples
Description
A dataframe containing average age of marriage, death, and number offspring by year for individuals in China.
Usage
1 |
Format
A data frame with 12 observations on the following 7 variables.
in.yearA numeric vector giving the input year.
out.yearA numeric vector giving the output year.
female.age.marriageA numeric vector giving the average age of marriage for females by year.
male.age.marriageA numeric vector giving the average age of marriage for males by year.
female.age.deathA numeric vector giving the average age of death for females by year.
male.age.deathA numeric vector giving the average age of death for males by year.
offspringA numeric vector giving the average numbers of offspring for families by year.
Source
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
Examples
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)
|
Example output
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
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.