lm（）在mutate（）中调用

问题描述

我想知道是否可以在dplyr包的mutate（）中使用lm（）。目前我有一个date，company，return和market.ret的数据框可重现如下：

I wonder if it is possible to use lm() within mutate() of dplyr package. Currently I have a dataframe of "date", "company", "return" and "market.ret" reproducible as below:

library(dplyr)
n.dates <- 60
n.stocks <- 2
date <- seq(as.Date("2011-07-01"), by=1, len=n.dates)
symbol <- replicate(n.stocks, paste0(sample(LETTERS, 5), collapse = ""))
x <- expand.grid(date, symbol)
x$return <- rnorm(n.dates*n.stocks, 0, sd = 0.05)
names(x) <- c("date", "company", "return")
x <- group_by(x, date)    
x <- mutate(x, market.ret = mean(x$return, na.rm = TRUE))

现在，我想要适应每个要market.ret返回的公司，计算线性回归系数和存储一个新的列的斜坡。我想用mutate（）这样做，但下面的代码不起作用：

Now for each company I would like to fit "return" by "market.ret", calculate the linear regression coefficient and store the slopes in a new column. I wish to do it with mutate(), but the below code does not work:

x <- group_by(x, company)
x <- mutate(x, beta = coef(lm(x$return~x$market.ret))[[2]])

R所报告的错误是：

Error in terms.formula(formula, data = data) : 
invalid term in model formula

提前感谢对于任何建议！

Thanks in advance for any suggestion!

推荐答案

这似乎适用于我：

group_by(x, company) %>%
    do(data.frame(beta = coef(lm(return ~ market.ret,data = .))[2])) %>%
    left_join(x,.)

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