Description Usage Arguments Value Examples
Simulate a missing vector with block missing pattern.
1 | MissSimulation(n = 84, maxlen = 15, cnst = 15, prob = 0.03)
|
n |
the length of the vector |
maxlen |
the maximum length of missing |
cnst |
the constant used to smooth the block missing |
prob |
the probability a single element in the vector gets missing |
the same length vector with wanted block missing pattern
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 | # default setting
rev1 <- MissSimulation()
# with larger missing probability
rev2 <- MissSimulation(prob = 0.5)
sum(is.na(rev1))
sum(is.na(rev2))
## Simulation block missing pattern in the Murray-Darling Basin rainfall data
BlockMissing <- function() {
complete.chunk <- data(complete.chunk)
block.size <- 3 # scale for blocks when simulating the first part
n.years <- c(12, 36, 48, 48) # number of years for four simulation parts
n.stations <- c(17, 17, 37, 24) # number of stations for four simulation parts
n.prob <- c(0.05, 0.005, 0.005, 0.0005) # probability vector for each simulation part
part1.sim <- function() MissSimulation(n = 4*n.years[1], maxlen=12, cnst=12, n.prob[1])
part2.sim <- function() MissSimulation(n = 12*n.years[2], maxlen=3, cnst=3, n.prob[2])
part3.sim <- function() MissSimulation(n = 12*n.years[3], maxlen=3, cnst=3, n.prob[3])
part4.sim <- function() MissSimulation(n = 12*n.years[4], maxlen=3, cnst=3, n.prob[4])
p1 <- function() {
part1.mat <- matrix(0, nrow = 4*n.years[1], ncol = n.stations[1])
for (j in 1:length(part1.mat[1, ])) {
part1.mat[, j] <- part1.sim()
part1.missing.mat <- matrix(0, nrow = 12*n.years[1], ncol = n.stations[1])
# each block value should repeate three times to get the true missing matrix
part1.missing.mat[1:nrow(part1.missing.mat), ] <- part1.mat[rep(1:nrow(part1.mat),
each=block.size), ]
part1.missing.mat[part1.missing.mat==1] <- NA
}
return(p1.miss = part1.missing.mat)
}
p2 <- function() {
# simulate missing matrix part2
part2.mat <- matrix(0, nrow=12*n.years[2], ncol=n.stations[2])
for (j in 1:length(part2.mat[1, ])) {
part2.mat[, j] <- part2.sim()
part2.missing.mat <- part2.mat
part2.missing.mat[part2.missing.mat==1] <- NA
}
return(p2.miss = part2.missing.mat)
}
p3 <- function() {
# simulate missing matrix part3
part3.mat <- matrix(0, nrow=12*n.years[3], ncol=n.stations[3])
for (j in 1:length(part3.mat[1, ])) {
part3.mat[, j] <- part3.sim()
part3.missing.mat <- part3.mat
part3.missing.mat[part3.missing.mat==1] <- NA
}
return(p3.miss = part3.missing.mat)
}
p4 <- function() {
# simulate missing matrix part3
part4.mat <- matrix(0, nrow=12*n.years[4], ncol=n.stations[4])
for (j in 1:length(part4.mat[1, ])) {
part4.mat[, j] <- part4.sim()
part4.missing.mat <- part4.mat
part4.missing.mat[part4.missing.mat==1] <- NA
}
return(p4.missing=part4.missing.mat)
}
return(complete.sim = as.data.frame(cbind(rbind(p2(), p1()), cbind(p3(),p4())))
+ complete.chunk)
}
# NOTRUN
# bdata <- BlockMissing()
# HeatStruct(bdata)
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