使用ggplot2

Question

我目前正在使用正态分布运行模拟，它模拟事件之间的时间，并基于对给定数据的分析（与问题无关）。仿真创建如下：

SimProcess <- function(mu, sigma, T) {

  ctimes <- c()  # Array of arrival times, initially empty
  t <- rnorm(1,mu, sqrt(sigma)) # Time of next arrival
  while(t < T) {
    ctimes <- c(ctimes, t)
    dt = rnorm(1, mu, sqrt(sigma))
    if (dt<0){dt = 0}
    t <- t + dt # sampling from the dataset
  }
  return(ctimes)
}

# Create a sample path of one run
T <- 10
# arrival times
arrivals <- SimProcess(mu_t, var_t, T)

现在我想做几个随机试验，然后将它们绘制成一个图，这样我们就可以将它与给定的数据进行比较。其中10项试验是理想的。我试着像这样绘图，但不幸的是它没有用。我恐怕我不得不使用reshape2来融合10个试验的数据，因为这些载体的长度都不一样。我用它来试图绘制所有的线条，它显然不会按照应有的方式工作。

x <- c(0, arrivals, T,rep(0,500-length(arrivals)))
y <- c(0:length(arrivals), length(arrivals),rep(0,500-length(arrivals)))
plotdataNT = data.frame(x,y)
p = ggplot(plotdataNT,aes(x,y))
plot(x,y,type = 's')
j = 1
for (j in 10){
  arrivals <- SimProcess(mu_t,var_t,T)
  x <- c(0, arrivals, T,rep(0,500-length(arrivals)))
  y <- c(0:length(arrivals), length(arrivals),rep(0,500-length(arrivals)))
  p = p + geom_step(mapping = aes (x,y))
}

编辑： 最后我把它弄清楚了，因为我用了10而不是1:10它不能正常运行而且我还有一些更小的错误。这最终成为解决方案：

arrivals <- SimProcess(mu_t,var_t,T)
NT <- length(arrivals)
x <- c(0, arrivals, T,rep(0,correction-length(arrivals)))
y <- c(0:length(arrivals), length(arrivals),rep(0,correction-length(arrivals)))
plotdataNT = data.frame(x,y)
p = ggplot(plotdataNT,aes(x,y)) + geom_step(mapping = aes (x,y)) 
jk = 1
runs = 25
colourvec = rainbow(runs)
for (jk in 1:runs){
  arrivals <- SimProcess(mu_t,var_t,T)
  x <- c(0, arrivals, T,rep(0,correction-length(arrivals)))
  y <- c(0:length(arrivals), length(arrivals),rep(0,correction-length(arrivals)))
  newdata  = data.frame(x,y)
  p = p + geom_step(mapping = aes (x,y),newdata,colour = colourvec[jk])
}
p = p + scale_x_continuous(name = "Time in days") + scale_y_continuous(name = "Amount of claims")
p  

这导致26个随机样本在一个图中以多种颜色绘制，它表示根据伽马，正态或对数正态分布的随机时间步长的过程。下面的答案是一个更清晰的例子，我的意思。如果有人知道如何以更有效的方式使用reshape2这样做，我也很高兴知道。

Answer 1

两种解决方案：

for (j in 1:10) {
  arrivals <- SimProcess(mu_t,var_t,T)
  x <- c(0, arrivals, T,rep(0,500-length(arrivals)))
  y <- c(0:length(arrivals), length(arrivals),rep(0,500-length(arrivals)))
  xy <- data.frame(x,y)
  p = p + geom_step(data=xy, mapping=aes(x,y))
}
print(p)

for (j in 1:10) {
  arrivals <- SimProcess(mu_t,var_t,T)
  x <- c(0, arrivals, T,rep(0,500-length(arrivals)))
  y <- c(0:length(arrivals), length(arrivals),rep(0,500-length(arrivals)))
  xy <- data.frame(x,y)
  p = p + geom_step(mapping=aes_string(x,y))
}
print(p)