Simulate a missing vector with block missing pattern.

Description

Simulate a missing vector with block missing pattern.

Usage

1
MissSimulation(n = 84, maxlen = 15, cnst = 15, prob = 0.03)

Arguments

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

Value

the same length vector with wanted block missing pattern

Examples

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# 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)