我想按组计算侧倾相关性，使用rollapply在组中找到一些问题
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我尝试使用分组依据来计算每个基金的相关性
usmergetemp%>%group_by(usmergetemp$fundid)
%>%mutate(
rollapplyr(usmergetemp, 3, function(usmergetemp) {
cor(usmergetemp[,2], usmergetemp[,3])
}, by.column = FALSE,fill=NA)
)
它们具有相同的行，应该可以工作，但是不能
mutate_impl(.data, dots)中的错误：列rollapplyr(usmergetemp, 3, function(usmergetemp) {cor(usmergetemp[, 2], usmergetemp[, 3])},by.column = FALSE, fill = NA)长度必须为153（组的大小）或一个，而不是300
我该如何解决此问题…… 谢谢您的帮助
答案 0 :(得分：0)
请考虑我上面的评论。
针对您的特定问题，我们可以执行以下操作：
library(dplyr)
library(zoo)
usmergetemp %>%
group_by(fundid) %>%
mutate(cor = rollapplyr(
data = cbind(return, sp500monthly),
width = 3,
FUN = function(w) cor(w[, 1], w[, 2]),
by.column = FALSE,
fill = NA))
## A tibble: 300 x 4
## Groups: fundid [4]
# fundid return sp500monthly cor
# <int> <dbl> <dbl> <dbl>
# 1 105 0.0486 0.0255 NA
# 2 105 0.0152 0.000453 NA
# 3 105 0.0172 0.0111 0.927
# 4 105 0.0125 0.0122 -0.163
# 5 105 -0.0741 -0.0309 0.998
# 6 105 -0.00785 0.0000866 0.999
# 7 105 -0.0111 0.00509 0.985
# 8 105 0.0184 0.0213 0.947
# 9 105 0.0259 0.0246 0.999
#10 105 0.0413 0.0315 1.000
## ... with 290 more rows
或者您可以使用tidyr::nest
library(tidyverse)
usmergetemp %>%
group_by(fundid) %>%
nest() %>%
mutate(data = map(data, ~bind_cols(.x, cor = rollapplyr(
data = .x,
width = 3,
FUN = function(w) cor(w[, 1], w[, 2]),
by.column = FALSE,
fill = NA)))) %>%
unnest()