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
203
204
205
206
207
208
209
210
211
212
|
######################################
## code for sample size simulations ##
## Nicholas Reich
## August 2010
######################################
## this is the central user function
## N = overall sample size
## med = median of log normal distribution
## disp = dispersion of log normal distribution
## percentile = what percentile(s) should be simulated
## nsim = how many simulations to run
## exact data = T/F should simulation be done with exact data
## pct.type.A = percent type A data, will be rounded up to nearest integer
## exp.win.dat = a vector of exposure window lengths to sample from
## verb = whether to print 10 iteration counts during the course of the sim
##' @name precision.simulation
##' @aliases precision.simulation.exact
##' @aliases precision.simulation.coarse
##' @aliases generate.coarse.data
##'
##' @title Simulate incubation period analyses with coarse data
##'
##' @description These functions simulate coarse incubation period data sets and
##' analyze them. The goal is for these simulations to provide evidence for
##' how much information a given dataset contains about a characteristic of
##' the incubation period distribution.
##'
##' @param N Overall sample size for the datasets to be simulated.
##' @param med Median for the assumed log normal distribution of the incubation
##' periods.
##' @param disp Dispersion for the assumed log normal distribution of the
##' incubation periods.
##' @param percentile Percentile of the incubation period distribution which we
##' want to estimate.
##' @param nsim Number of datasets to analyze in the simulation.
##' @param exact.data Either TRUE/FALSE. Incidates whether the data generated
##' should be coarsened at all. If TRUE, pct.type.A and exp.win.dat are
##' ignored.
##' @param pct.type.A Percent of the N observations that are assumed to be type
##' A data. If N*pct.type.A is not an integer, it will be rounded to the
##' nearest integer.
##' @param exp.win.dat A vector of exposure window lengths. Defaults to the
##' observed window lengths from Lessler et al. (see below).
##' @param verb If TRUE, a message with the system time and iteration number
##' will be printed ten times during the simulation run.
##'
##'
##' @rdname precision.simulation
##' @return The \code{precision.simulation} functions return a matrix with four
##' columns and nsim rows. The "ests" column gives the estimated percentiles
##' for the incubation period distribution. The "SE" column gives the
##' standard error for the estimate. The "conv" column is 1 if the doubly
##' interval-censored likelihood maximization converged. Otherwise, it is 0.
##' The "bias" column gives the estimated percentile - true percentile. The
##' \code{generate.coarse.data} function returns a matrix with data suitable
##' for analysis by the \code{dic.fit} function.
##' @export
precision.simulation <- function(N,
med=2,
disp=1.3,
percentile=.5,
nsim=100,
exact.data=FALSE,
pct.type.A=.5,
exp.win.dat=NULL,
verb=FALSE) {
## logic check
if(percentile <= 0 | percentile >=1)
stop("percentile must be between 0 and 1.")
if(pct.type.A < 0 | pct.type.A >1)
stop("% of data that is type A must be between 0 and 1.")
if(is.null(exp.win.dat)){
if(verb) message("NYC exposure window data used")
exp.win.dat <- get(data(exp.win.lengths, envir = environment()))
}
## TODO: add default exp.win.dat to package and load if NULL
if(exact.data) {
out <- precision.simulation.exact(N=N,
med=med,
disp=disp,
percentile=percentile,
nsim=nsim,
verb=verb)
} else {
out <- precision.simulation.coarse(N=N,
med=med,
disp=disp,
percentile=percentile,
nsim=nsim,
pct.type.A=pct.type.A,
exp.win.dat=exp.win.dat,
verb=verb)
}
target <- qlnorm(percentile, log(med), log(disp))
bias <- out[,"ests"]-target
out <- cbind(out, bias)
return(out)
}
##' @rdname precision.simulation
##' @export
precision.simulation.exact <- function(N,
med,
disp,
percentile,
nsim,
verb) {
storage <- matrix(NA, ncol=3, nrow=nsim)
colnames(storage) <- c("ests", "SE", "conv")
data <- matrix(0, ncol=6, nrow=nsim*N)
colnames(data) <- c("dataset.id", "EL", "ER", "SL", "SR", "type")
data[,"dataset.id"] <- rep(1:nsim, each=N)
data[,"SL"] <- rlnorm(N*nsim, meanlog=log(med), sdlog=log(disp))
data[,"SR"] <- data[,"SL"]
data[,"type"] <- 2
for(i in 1:nsim){
tmp.dat <- data[which(data[,"dataset.id"]==i),]
tmp.fit <- dic.fit(tmp.dat, ptiles=percentile)
if(tmp.fit$conv==1){
row.name <- paste("p", round(percentile*100), sep="")
which.row <-
which(rownames(tmp.fit$ests)==row.name)[1]
storage[i,c("ests", "SE")] <-
tmp.fit$ests[which.row, c("est", "StdErr")]
} else {
storage[i,c("ests", "SE")] <- NA
}
storage[i,"conv"] <- tmp.fit$conv
if(verb & i%%(round(nsim/10))==0)
print(paste("iteration",i,"complete ::", Sys.time()))
}
return(storage)
}
##' @rdname precision.simulation
##' @export
precision.simulation.coarse <- function(N,
med,
disp,
percentile,
nsim,
pct.type.A,
exp.win.dat,
verb) {
## create storage
storage <- matrix(NA, ncol=3, nrow=nsim)
colnames(storage) <- c("ests", "SE", "conv")
for(i in 1:nsim){
tmp.dat <- generate.coarse.data(N=N,
med=med,
disp=disp,
pct.type.A=pct.type.A,
exp.win.dat=exp.win.dat)
tmp.fit <- dic.fit(tmp.dat, ptiles=percentile)
if(tmp.fit$conv==1){
row.name <- paste("p", round(percentile*100), sep="")
which.row <-
which(rownames(tmp.fit$ests)==row.name)[1]
storage[i,c("ests", "SE")] <-
tmp.fit$ests[which.row, c("est", "StdErr")]
} else {
storage[i,c("ests", "SE")] <- NA
}
storage[i,"conv"] <- tmp.fit$conv
if(verb & i%%(round(nsim/10))==0)
print(paste("iteration",i,"complete ::", Sys.time()))
}
return(storage)
}
##' @rdname precision.simulation
##' @export
generate.coarse.data <- function(N, med, disp, pct.type.A, exp.win.dat) {
n.type.A <- round(N*pct.type.A)
n.type.B <- N-n.type.A
E <- runif(N, 10, 11)
T <- rlnorm(N, log(med), log(disp))
S <- T + E
SR <- ceiling(S)
SL <- floor(S)
## generate window types
## 0 = short with bounded ER = type A
## 1 = "long" with no ER = type B
win.type <- rep(0:1, times=c(n.type.A, n.type.B))
## generate window lengths,
potential.lengths <- sample(exp.win.dat, size=N, replace=TRUE)
win.length <- 1*(win.type==0) + potential.lengths*(win.type>0)
## fix data
ER <- ceiling(E)*(win.type==0) + SR*(win.type==1)
EL <- pmin(ER-win.length, floor(E))
## return the data
cbind(EL, E, ER, SL, S, SR, win.length, win.type, type=0)
}
|
