是否有用于从频率表计算一阶转换矩阵的 R 包? [英] Is there an R package to calculate 1st order transition matrix from a frequency table?

问题描述

我有一个由 8 亿条记录聚合而成的频率表，我想知道是否可以使用包从频率表中计算一阶转移矩阵，这是不对称的，因为某些状态再也没有发生过.频率表的一个样本是:

I have a frequency table aggregated from 800 millions of records and am wondering if I can use a package to calculate 1st order transition matrix from the frequency table, which is not symmetric because some state just never happened again. A sample of the frequency table is:

library(data.table)
model.data <- data.table(state1 = c(3, 1, 2, 3), state2 = c(1, 2, 1, 2), Freq = c(1,2,3,4))

model.data 看起来像这样:

model.data looks like this:

<头>

state1	state2	n
3	1	1
1	2	2
2	1	3
3	2	4

使用包 pollster，我可以计算出比例表:

Using the package pollster, I can compute the proportion table:

library(pollster)
crosstab(model.data, state1, state2, Freq)

<头>

state1	1	2	n
1	0	100	2
2	100	0	3
3	20	80	5

然而，我正在寻找的对称转移矩阵是:

However, the symmetric transition matrix I am looking for is:

<头>

state1	1	2	3	n
1	0	100	0	2
2	100	0	0	3
3	20	80	0	5

也就是说，即使没有人转换到状态3，我仍然想包含状态3，并且代码应该能够自动找出3需要附加一列0.

That is, I still want to include the state 3 even though no one transitioned to it, and the code should be able to automatically find out 3 needs to be appended with a column of 0s.

由于内存限制和缓慢的计算速度，我不确定带有 markovchainFit 函数的 markovchain 包是否能够处理我需要转换为数百万序列列表的 8 亿行数据.

I am not sure if the markovchain package with the markovchainFit function is going to handle my 800 million rows of data that I need to transform into a list of millions of sequences, due to memory constraints and slow computing speed.

有人知道吗?

推荐答案

An option with igraph

An option with igraph

model.data %>%
  setorder(state1) %>%
  graph_from_data_frame() %>%
  as_adjacency_matrix(attr = "Freq", sparse = FALSE) %>%
  proportions(1)  # 1 sets rows as the margin, similar to `prop.table`

给予

    1   2 3
1 0.0 1.0 0
2 1.0 0.0 0
3 0.2 0.8 0

---

或使用基数 R

---

Or with base R

> proportions(xtabs(Freq ~ ., model.data), 1)
      state2
state1   1   2
     1 0.0 1.0
     2 1.0 0.0
     3 0.2 0.8

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