Question

我有多个因素的时间序列：

df = read.table(text="
    date        factor     stock    value
    30-Jun-17   DivYield    AAPL    0.05
    30-Jun-17   DivYield    GOOG    0.055
    30-Jun-17   DivYield    MSFT    0.02
    31-Jul-17   DivYield    AAPL    0.055
    31-Jul-17   DivYield    GOOG    0.05
    31-Jul-17   DivYield    MSFT    0.025
    30-Jun-17   PB          AAPL    12
    30-Jun-17   PB          GOOG    11
    30-Jun-17   PB          MSFT    16
    31-Jul-17   PB          AAPL    11
    31-Jul-17   PB          GOOG    12
    31-Jul-17   PB          MSFT    14
    30-Jun-17   ROE         AAPL    0.1
    30-Jun-17   ROE         GOOG    0.12
    30-Jun-17   ROE         MSFT    0.12
    31-Jul-17   ROE         AAPL    0.1
    31-Jul-17   ROE         GOOG    0.1
    31-Jul-17   ROE         MSFT    0.12
            ", header = TRUE)
df$date = lubridate::dmy(df$date)

我需要计算因子之间的成对相关性，我需要每天都这样做。 Pearson相关结果看起来像是：

Date        Factor1  Factor2 Correlation.Time.Series
30-Jun-17   DivYield    PB      -0.998337488
30-Jun-17   DivYield    ROE     -0.381246426
30-Jun-17   PB          ROE     0.327326835
31-Jul-17   DivYield    PB      -0.984324138
31-Jul-17   DivYield    ROE     -0.987829161
31-Jul-17   PB          ROE     0.944911183

关于如何攻击这个的任何想法？

这是我的第一次尝试：

library(tidyverse)
df.spread = spread(df, key = factor, value = value)
first.attempt = df.spread %>%
    select(-stock) %>%
    group_by(date) %>%
    do(as.data.frame(cor(.[,-1])))

这似乎是这样做的。问题是输出没有标签显示我的相关性：

        date   DivYield        PB         ROE
1 2017-06-30  1.0000000 -0.9983375 -0.3812464
2 2017-06-30 -0.9983375  1.0000000  0.3273268
3 2017-06-30 -0.3812464  0.3273268  1.0000000
4 2017-07-31  1.0000000 -0.9843241 -0.9878292
5 2017-07-31 -0.9843241  1.0000000  0.9449112
6 2017-07-31 -0.9878292  0.9449112  1.0000000

Answer 1

查看ItemDetailsView包。这与corrr组合一起将为您提供一列rownames，以便您可以匹配相关对。

mutate + map

这会给你：

df.spread %>%
  select(-stock) %>%
  group_by(date) %>%
  nest() %>%
  mutate(cor_tbls = map(data, ~corrr::correlate(.x))) %>%
  unnest(cor_tbls)

如何计算成对相关的时间序列

1 个答案: