如何先 group_by() 然后迭代 lm() 通过列？

Question

假设我们有一个数据框，其中包含一组由分组变量标记的 3 个因变量和 6 个自变量。使用以下示例代码生成此格式的示例：

library(tidyverse)
library(broom)
n  <- 15
df  <- data.frame(groupingvar= sample(letters[1:2], size = n, replace = TRUE),
                  y1 = rnorm(n,10,1), y2=rnorm(n,100,10), y3=rnorm(n,1000,100),
                  x1=  rnorm(n,10,1), x2=rnorm(n,10,1), x3=rnorm(n,10,1),
                  x4=rnorm(n,10,1), x5=rnorm(n,10,1), x6=rnorm(n,10,1))
df <- arrange(df,groupingvar)

如果我想对 x1 到 x6 的集合中的每个 y1、y2、y3 进行回归，我可以使用类似以下内容的方法：

y <- as.matrix(select(df,y1:y3))
x <- as.matrix(select(df,x1:x6))
regs <-lm(y~x)
coeffs <- tidy(regs)
coeffs <- arrange(coeffs,response, term)

（通过使用 lm() 帮助中的以下行："If response is a matrix, a linear model is fitted separately by least-squares to each column of the matrix."）

但是，如果我需要先按分组变量进行分组，然后再应用 lm 函数，那么我不太确定该怎么做。我尝试了以下方法，但它为两组生成了相同的系数集。

regs2 <- df %>% group_by(groupingvar) %>%
  do(fit2 = lm(as.matrix(select(df,y1:y3)) ~ as.matrix(select(df,x1:x6))))
coeffs2 <- tidy(regs2,fit2)
coeffs2 <- arrange(coeffs2,groupingvar, response)

Answer 1

在 data.table 中，您可以 melt（重塑 long -- 将结果变量堆叠在一列中而不是存储在三列中）& lm groupingvar 和结果变量：

library(data.table)
setDT(df)

#alternatively, set id.vars = c('groupingvar', paste0('x', 1:6)), etc.
longDT = melt(df, id.vars = grep('y', names(df), invert = TRUE))

#this helper function basically splits a named vector into
#  its two components
coefsplit = function(reg) {
  beta = coef(reg)
  list(var = names(beta), coef = beta)
}

#I personally wouldn't assign longDT, I'd just chain this onto
#  the output of melt;
longDT[ , coefsplit(lm(value ~ ., data = .SD)), by = .(groupingvar, variable)]
#     groupingvar variable         var          coef
#  1:           a       y1 (Intercept) -3.595564e+03
#  2:           a       y1          x1 -3.796627e+01
#  3:           a       y1          x2 -1.557268e+02
#  4:           a       y1          x3  2.862738e+02
#  5:           a       y1          x4  1.579548e+02
# ...
# 38:           b       y3          x2  2.136253e+01
# 39:           b       y3          x3 -3.810176e+01
# 40:           b       y3          x4  4.187719e+01
# 41:           b       y3          x5 -2.586184e+02
# 42:           b       y3          x6  1.181879e+02
#     groupingvar variable         var          coef

Answer 2

我还找到了一种使用 cbind() 实现此目的的方法，如下所示：

library(tidyverse)
library(broom)
n  <- 20
df4  <- data.frame(groupingvar= sample(1:2, size = n, replace = TRUE),
                   y1 = rnorm(n,10,1), y2=rnorm(n,100,10), y3=rnorm(n,1000,100),
                   x1=  rnorm(n,10,1), x2=rnorm(n,10,1), x3=rnorm(n,10,1),
                   x4=rnorm(n,10,1), x5=rnorm(n,10,1), x6=rnorm(n,10,1))
df4 <- arrange(df4,groupingvar)

regs <- df4 %>% group_by(groupingvar) %>%
  do(fit = lm(cbind(y1,y2,y3) ~ . -groupingvar, data = .))
coeffs <- tidy(regs, fit)

如何先 group_by() 然后迭代 lm() 通过列？

How to first group_by() then iterate lm() through columns?

r

tidy

dplyr

broom