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r - 绘制时间步长？

问题描述

我想绘制转换概率与时间的关系。

The dimension of the ["Activities"][1] matrix is ncol=144 and nrows=16533; act1_1...ac1_144 are time-steps, and time is represented in 10 minutes intervals (e.g. act1_1 = 4.10am; act1_2=4.20am..). Time start from 4am (act1_1) and ends at act1_144(4am).The columns are filled in with different activities, such 2=sleep, 48=watching Tv, 5=eating, etc.

使用这个函数，我设法计算了活动之间的转移概率（活动矩阵）。

我想绘制 x 轴时间（10 分钟间隔）和 y 轴概率，例如 2 跟随 3。

我怎样才能做到这一点？

谢谢

这是我想要的情节

[1]：https://i.stack.imgur.com/1UEUw.jpg

标签： rmatrixggplot2plottransition

解决方案

要绘制转换概率随时间的变化，您（根据定义）需要多次观察从一个动作a到另一个动作的转换b。

一旦你有多个交易概率矩阵，绘制它们随时间的发展实际上是非常简单的。在下面的示例中，大部分代码实际上都专门用于（重新）创建您的数据和不同的转移概率矩阵。

library(ggplot2)

num_samples <- 1000
num_actions <- 20

# 1. generate activities dataframe (dropped ID column):
activ <- data.frame(v1 = sample(1:num_actions, num_samples, replace = TRUE), 
                    v2 = sample(1:num_actions, num_samples, replace = TRUE), 
                    v3 = sample(1:num_actions, num_samples, replace = TRUE), 
                    v4 = sample(1:num_actions, num_samples, replace = TRUE), 
                    v5 = sample(1:num_actions, num_samples, replace = TRUE), 
                    v6 = sample(1:num_actions, num_samples, replace = TRUE), 
                    v7 = sample(1:num_actions, num_samples, replace = TRUE))

num_transp <- ncol(activ) - 1 

# 2. calculate transition probabilities for each time step:
l_transp <- vector("list", num_transp)
for (t in 1:num_transp){
  transp <- matrix(0, nrow = num_actions, ncol = num_actions)
  data = activ[, t:(t+1)]
  for (action in 1:num_actions){
    rows_to_keep <- data[,1] == action
    counts <- table(data[rows_to_keep,])
    probs <- as.data.frame(counts/sum(rows_to_keep))
    follow_actions <- as.integer(as.character(probs[,2]))
    transp[action, follow_actions] <- probs$Freq
  }
  l_transp[[t]] <- transp
}

# 3. get development of transition probability from action 2 to action 3 over time:
to_plot <- vector("numeric", num_transp)
for (i in 1:num_transp){
  to_plot[i] <- l_transp[[i]][2, 3]
}

# plot development of transition probability from action 2 to action 3 over time:
ggplot(data.frame(x=1:num_transp, y=to_plot), aes(x=x, y=y)) +
  geom_line() + 
  xlab("time") +
  ylab("transition probability")

运行它会给你类似的东西这

它看起来非常像人们对随着时间的推移随机且独立地选择 20 个不同动作的场景所期望的那样。

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