-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsimulation_bc.R
203 lines (175 loc) · 10.3 KB
/
simulation_bc.R
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
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
#' Simulation with nested approach
#'
#' @param S integer; number of simulation steps
#' @param ssize vector; sample sizes, e.g., c(400, 200, 100, 50)
#' @param linfreq vector; lineage-frequency distribution
#' @param path path to the file
#' @param lambda numeric; MOI parameter
#'
#' @return the simulation results are stored in a txt file specified by path
#' @export
#'
simu_CP <- function(S, ssize, linfreq, path, lambda){
n <- length(linfreq)
p <- as.vector(round(linfreq, digits = 2))
sz <- length(ssize)
ssize <- sort(ssize, decreasing = T)
NN <- ssize[1]
simfinal <- rep(list(matrix(NA, S, 21 + 5*n)), sz)
sim <- 0
while (sim < S){
M <- sign(mnom(cpoiss(lambda, NN), p))
NkN <- colSums(M)
if (sum(NkN) <= NN || max(NkN) == NN){
}else{
sim <- sim + 1
mleN <- MLE(NN, NkN)
bcmleN <- BCMLE(NN, NkN)
hbcmle1N <- HBCMLE1(NN, NkN)
hbcmle2N <- HBCMLE2(NN, NkN)
hbcmle3N <- HBCMLE3(NN, NkN)
est_lambda_psi_N <- c(mleN[[1]], mleN[[2]], mleN[[3]],
bcmleN[[1]], bcmleN[[2]],
hbcmle1N[[1]], hbcmle1N[[2]],
hbcmle2N[[1]], hbcmle2N[[2]],
hbcmle3N[[1]], hbcmle3N[[2]])
mlepn <- mleN[[4]]
bcmlepn <- bcmleN[[3]]
bcmleqpn <- hbcmle1N[[3]]
bcmleqqpn <- hbcmle2N[[3]]
bcmleqqqpn <- hbcmle3N[[3]]
eun <- sqrt(sum((p - mlepn)^2)) ##Euclidean distance between true p and mle of p
eubcn <- sqrt(sum((p - bcmlepn)^2)) ##Euclidean distance between true p and bias corrected
eubcqn <- sqrt(sum((p - bcmleqpn)^2)) ##Euclidean distance between true p and bias corrected
eubcqqn <- sqrt(sum((p - bcmleqqpn)^2)) ##Euclidean distance between true p and bias corrected
eubcqqqn <- sqrt(sum((p - bcmleqqqpn)^2)) ##Euclidean distance between true p and bias corrected
pn_nonzero <- p[mlepn>0]
mlepn_nonzero <- mlepn[mlepn>0]
bcmlepn_nonzero <- bcmlepn[bcmlepn>0]
bcmleqpn_nonzero <- bcmleqpn[bcmleqpn>0]
bcmleqqpn_nonzero <- bcmleqqpn[bcmleqqpn>0]
bcmleqqqpn_nonzero <- bcmleqqqpn[bcmleqqqpn>0]
kln <- sum(mlepn_nonzero*log(mlepn_nonzero/pn_nonzero)) ##Kullback-Leibler between true p and mle of p
klbcn <- sum(bcmlepn_nonzero*log(bcmlepn_nonzero/pn_nonzero)) ##Kullback-Leibler between true p and bias corrected
klbcqn <- sum(bcmleqpn_nonzero*log(bcmleqpn_nonzero/pn_nonzero)) ##Kullback-Leibler between true p and bias corrected
klbcqqn <- sum(bcmleqqpn_nonzero*log(bcmleqqpn_nonzero/pn_nonzero)) ##Kullback-Leibler between true p and bias corrected
klbcqqqn <- sum(bcmleqqqpn_nonzero*log(bcmleqqqpn_nonzero/pn_nonzero)) ##Kullback-Leibler between true p and bias corrected
simfinal[[1]][sim,] <- c(est_lambda_psi_N,
mlepn, bcmlepn, bcmleqpn, bcmleqqpn, bcmleqqqpn,
eun, eubcn, eubcqn, eubcqqn, eubcqqqn,
kln, klbcn, klbcqn, klbcqqn, klbcqqqn)
k <- 1
for (N in ssize[-1]) {
k <- k + 1
Nk <- colSums(M[1:N,])
if (sum(Nk) <= N || max(Nk) == N){
Nk <- NA
newdata <- innersamplegenerator_CP(Nk,N,lambda,p,n)
M <- newdata[[1]]
Nk <- newdata[[2]]
mle <- MLE(N, Nk)
bcmle <- BCMLE(N, Nk)
hbcmle1 <- HBCMLE1(N, Nk)
hbcmle2 <- HBCMLE2(N, Nk)
hbcmle3 <- HBCMLE3(N, Nk)
est_lambda_psi <- c(mle[[1]], mle[[2]], mle[[3]],
bcmle[[1]], bcmle[[2]],
hbcmle1[[1]], hbcmle1[[2]],
hbcmle2[[1]], hbcmle2[[2]],
hbcmle3[[1]], hbcmle3[[2]])
mlep <- mle[[4]]
bcmlep <- bcmle[[3]]
bcmleqp <- hbcmle1[[3]]
bcmleqqp <- hbcmle2[[3]]
bcmleqqqp <- hbcmle3[[3]]
eu <- sqrt(sum((p - mlep)^2)) ##Euclidean distance between true p and mle of p
eubc <- sqrt(sum((p - bcmlep)^2)) ##Euclidean distance between true p and bias corrected
eubcq <- sqrt(sum((p - bcmleqp)^2)) ##Euclidean distance between true p and bias corrected
eubcqq <- sqrt(sum((p - bcmleqqp)^2)) ##Euclidean distance between true p and bias corrected
eubcqqq <- sqrt(sum((p - bcmleqqqp)^2)) ##Euclidean distance between true p and bias corrected
p_nonzero <- p[mlep>0]
mlep_nonzero <- mlep[mlep>0]
bcmlep_nonzero <- bcmlep[bcmlep>0]
bcmleqp_nonzero <- bcmleqp[bcmleqp>0]
bcmleqqp_nonzero <- bcmleqqp[bcmleqqp>0]
bcmleqqqp_nonzero <- bcmleqqqp[bcmleqqqp>0]
kl <- sum(mlep_nonzero*log(mlep_nonzero/p_nonzero)) ##Kullback-Leibler between true p and mle of p
klbc <- sum(bcmlep_nonzero*log(bcmlep_nonzero/p_nonzero)) ##Kullback-Leibler between true p and bias corrected
klbcq <- sum(bcmleqp_nonzero*log(bcmleqp_nonzero/p_nonzero)) ##Kullback-Leibler between true p and bias corrected
klbcqq <- sum(bcmleqqp_nonzero*log(bcmleqqp_nonzero/p_nonzero)) ##Kullback-Leibler between true p and bias corrected
klbcqqq <- sum(bcmleqqqp_nonzero*log(bcmleqqqp_nonzero/p_nonzero)) ##Kullback-Leibler between true p and bias corrected
simfinal[[k]][sim,] <- c(est_lambda_psi,
mlep, bcmlep, bcmleqp,bcmleqqp,bcmleqqqp,
eu, eubc, eubcq, eubcqq, eubcqqq,
kl, klbc, klbcq, klbcqq, klbcqqq)
}else{
mle <- MLE(N, Nk)
bcmle <- BCMLE(N, Nk)
hbcmle1 <- HBCMLE1(N, Nk)
hbcmle2 <- HBCMLE2(N, Nk)
hbcmle3 <- HBCMLE3(N, Nk)
est_lambda_psi <- c(mle[[1]], mle[[2]], mle[[3]],
bcmle[[1]], bcmle[[2]],
hbcmle1[[1]], hbcmle1[[2]],
hbcmle2[[1]], hbcmle2[[2]],
hbcmle3[[1]], hbcmle3[[2]])
mlep <- mle[[4]]
bcmlep <- bcmle[[3]]
bcmleqp <- hbcmle1[[3]]
bcmleqqp <- hbcmle2[[3]]
bcmleqqqp <- hbcmle3[[3]]
eu <- sqrt(sum((p - mlep)^2)) ##Euclidean distance between true p and mle of p
eubc <- sqrt(sum((p - bcmlep)^2)) ##Euclidean distance between true p and bias corrected
eubcq <- sqrt(sum((p - bcmleqp)^2)) ##Euclidean distance between true p and bias corrected
eubcqq <- sqrt(sum((p - bcmleqqp)^2)) ##Euclidean distance between true p and bias corrected
eubcqqq <- sqrt(sum((p - bcmleqqqp)^2)) ##Euclidean distance between true p and bias corrected
p_nonzero <- p[mlep>0]
mlep_nonzero <- mlep[mlep>0]
bcmlep_nonzero <- bcmlep[bcmlep>0]
bcmleqp_nonzero <- bcmleqp[bcmleqp>0]
bcmleqqp_nonzero <- bcmleqqp[bcmleqqp>0]
bcmleqqqp_nonzero <- bcmleqqqp[bcmleqqqp>0]
kl <- sum(mlep_nonzero*log(mlep_nonzero/p_nonzero)) ##Kullback-Leibler between true p and mle of p
klbc <- sum(bcmlep_nonzero*log(bcmlep_nonzero/p_nonzero)) ##Kullback-Leibler between true p and bias corrected
klbcq <- sum(bcmleqp_nonzero*log(bcmleqp_nonzero/p_nonzero)) ##Kullback-Leibler between true p and bias corrected
klbcqq <- sum(bcmleqqp_nonzero*log(bcmleqqp_nonzero/p_nonzero)) ##Kullback-Leibler between true p and bias corrected
klbcqqq <- sum(bcmleqqqp_nonzero*log(bcmleqqqp_nonzero/p_nonzero)) ##Kullback-Leibler between true p and bias corrected
simfinal[[k]][sim,] <- c(est_lambda_psi, mlep, bcmlep, bcmleqp,bcmleqqp,bcmleqqqp,
eu, eubc, eubcq, eubcqq, eubcqqq, kl, klbc, klbcq, klbcqq, klbcqqq)
}
}
}
}
for(d in 1:sz){
B <- c(lambda, apply(simfinal[[d]],2,mean),apply(simfinal[[d]],2,var), ssize[d], p)
write.table(t(B),paste(path,"/data-n",toString(n), "-maxfreq", round(p[1],digits = 2), ".txt",sep=""),
append=TRUE, sep=" ", col.names=FALSE, row.names=FALSE)
}
}
######################################################################
#' For nested simulation (above), generates random dataset of size N
#'
#' @param Nk integer vector; number of lineage prevalence counts in a dataset.
#' for a simulated data this is simply derived as \code{colSums(dataset)}. To derive
#' the MLE and lineage prevalence counts for a real dataset please refer to
#' the package \link[MLMOI]{moimle}.
#' @param N integer; sample size
#' @param lambda numeric; MOI parameter
#' @param p vector; lineage-frequency distribution
#' @param n integer; number of lineages
#'
#' @return new random of size N and Nk's
#' @export
#'
innersamplegenerator_CP <- function(Nk,N,lambda,p,n) {
while (is.na(Nk[1]) == TRUE){
MN <- sign(mnom(cpoiss(lambda, N), p))
Nk <- colSums(MN)
if (sum(Nk) <= N || max(Nk) == N){
Nk <- NA
}else{
M <- MN
}
}
list(M,Nk)
}