Repository navigation
Expand file tree
/
Copy pathUsingAll.R
More file actions
120 lines (89 loc) · 3.35 KB
/
Copy pathUsingAll.R
File metadata and controls
120 lines (89 loc) · 3.35 KB
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
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
# Setting up
install.packages("mispitools")
install.packages("pedtools")
library(mispitools)
library(pedtools)
library(forrel)
# Section 1: SE data
## Analyzing comparison data based LRs
### Section 1.1: Biological sex
sex_H1 <- LRsex("F", H = 1, LR = TRUE, seed = 123, nsims = 500)
sex_H2 <- LRsex("F", H = 2, LR = TRUE, seed = 123, nsims = 500)
sex_LRs <- as.data.frame(cbind(sex_H2$LRs, sex_H1$LRs))
names(sex_LRs) <- c("Unrelated", "Related")
sex_LRs
LRdist(sex_LRs)
Trates(sex_LRs, 1)
### Section 1.2: Age
age_H1 <- LRage(40, H = 1, LR = TRUE, seed = 123, nsims = 500)
age_H2 <- LRage(40, H = 2, LR = TRUE, seed = 123, nsims = 500)
age_LRs <- as.data.frame(cbind(age_H2$LRa, age_H1$LRa))
names(age_LRs) <- c("Unrelated", "Related")
age_LRs
LRdist(age_LRs)
Trates(age_LRs, 1)
### Section 1.3: Pigmentation trait
col_H1 <- LRcol(1, H = 1, LR = TRUE, seed = 123, nsims = 500)
col_H2 <- LRcol(1, H = 2, LR = TRUE, seed = 123, nsims = 500)
col_LRs <- as.data.frame(cbind(col_H2$LRc, col_H1$LRc))
names(col_LRs) <- c("Unrelated", "Related")
col_LRs
LRdist(col_LRs)
Trates(col_LRs, 1)
### Section 1.4: Combined comparison data
comb_H1 <- sex_H1$LRs * age_H1$LRa * col_H1$LRc
names(comb_H1) <- "LRH1"
comb_H2 <- sex_H2$LRs * age_H2$LRa * col_H2$LRc
names(comb_H2) <- "LRH2"
comb_LRs <- as.data.frame(cbind(comb_H2, comb_H1))
names(comb_LRs) <- c("Unrelated", "Related")
LRdist(comb_LRs)
Trates(comb_LRs, 1)
### Section 1.5: Visualization, exploring overall likelihoods and LRs
POPl <- CPT_POP(propS = c(0.5, 0.5),
MPa = 40,
MPr = 6,
propC = c(0.3, 0.2, 0.25, 0.15, 0.1))
### Note: Population frequencies are obtained from the reference datasets
MPl <- CPT_MP(MPs = "F", MPc = 1,
eps = 0.05, epa = 0.05,
epc = Cmodel())
MPl/POPl
CondPlot(MPl,POPl)
# Section 2: FDE data
## Simulating DNA-based LRs in underpowered cases
set.seed(1234)
f <- getfreqs(Argentina)
ped1 <- linearPed(3)
ped1 <- setMarkers(ped1,locusAttributes = f)
ped1 <- profileSim(ped1,N = 1,ids = c(2,4))
datasimx = simLRgen(ped1, missing = 7, numsims = 500, seed = 1234)
plot(ped1, hatched = typedMembers(ped1), cex = 0.9)
LRdist(datasimx)
Trates(datasimx, threshold = 1)
ped2 <- cousinPed(1)
ped2 <- setMarkers(ped2,locusAttributes = f)
ped2 <- profileSim(ped2,N = 1,ids = 8)
datasimy = simLRgen(ped2, missing = 7, numsims = 500, seed = 1234)
plot(ped2, hatched = typedMembers(ped2), cex = 1)
LRdist(datasimy)
Trates(datasimy, threshold = 1)
# Section 3: Combining FDE and SE data
## Combining DNA-based LRs and comparison data based LRs
#### Note: We consider the same SE data for both FDE cases
datasimx2 <- simLR2dataframe(datasimx)
combinedx_H1 <- datasimx2$Related * sex_H1$LRs * col_H1$LRc * age_H1$LRa
combinedx_H2 <- datasimx2$Unrelated * sex_H2$LRs * col_H2$LRc * age_H2$LRa
combined_datasimx <- as.data.frame(cbind(combinedx_H2, combinedx_H1))
names(combined_datasimx) <- c("Unrelated", "Related")
combined_datasimx
LRdist(combined_datasimx)
Trates(combined_datasimx, 1)
datasimy2 <- simLR2dataframe(datasimy)
combinedy_H1 <- datasimy2$Related * sex_H1$LRs * col_H1$LRc * age_H1$LRa
combinedy_H2 <- datasimy2$Unrelated * sex_H2$LRs * col_H2$LRc * age_H2$LRa
combined_datasimy <- as.data.frame(cbind(combinedy_H2, combinedy_H1))
names(combined_datasimy) <- c("Unrelated", "Related")
combined_datasimy
LRdist(combined_datasimy)
Trates(combined_datasimy, 1)