使用broom和tidyverse对不同的因变量进行回归 [英] Use broom and tidyverse to run regressions on different dependent variables

问题描述

我正在寻找可以解决这个难题的Tidyverse /扫帚解决方案：

I'm looking for a Tidyverse / broom solution that can solve this puzzle:

假设我有不同的DV 和<特定于IVS的 strong>特定集，我想进行回归分析，考虑每个DV和该特定IV的集合。
我知道我可以在自己的家庭中使用类似的东西，也可以申请家庭，但我真的想使用 tidyverse 来运行它。

Let's say I have different DVs and a specific set of IVS and I want to perform a regression that considers every DV and this specific set of IVs. I know I can use something like for i in or apply family, but I really want to run that using tidyverse.

以下代码作为示例

ds <- data.frame(income = rnorm(100, mean=1000,sd=200),
                 happiness = rnorm(100, mean = 6, sd=1),
                 health = rnorm(100, mean=20, sd = 3),
                 sex = c(0,1),
                 faculty = c(0,1,2,3))

mod1 <- lm(income ~ sex + faculty, ds)
mod2 <- lm(happiness ~ sex + faculty, ds)
mod3 <- lm(health ~ sex + faculty, ds)
summary(mod1)
summary(mod2)
summary(mod3)

收入，幸福和健康是DV。 Sex and Faculty是IV，它们将用于所有回归。

Income, happiness, and health are DVs. Sex and Faculty are IVs and they will be used for all regressions.

那是我发现的最接近的

让我知道是否需要澄清我的问题。
谢谢。

Let me know If I need to clarify my question. Thanks.

推荐答案

由于您有不同的因变量，但有相同的独立变量，因此可以形成这些变量的矩阵，传递给 lm 。

As you have different dependent variables but the same independent, you can form a matrix of these and pass to lm.

mod = lm(cbind(income, happiness, health) ~ sex + faculty, ds)

我认为扫帚： ：tidy 作品

library(broom)
tidy(mod)

#    response        term      estimate  std.error  statistic      p.value
# 1    income (Intercept) 1019.35703873 31.0922529 32.7849205 2.779199e-54
# 2    income         sex  -54.40337314 40.1399258 -1.3553431 1.784559e-01
# 3    income     faculty   19.74808081 17.9511206  1.1001030 2.740100e-01
# 4 happiness (Intercept)    5.97334562  0.1675340 35.6545278 1.505026e-57
# 5 happiness         sex    0.05345555  0.2162855  0.2471528 8.053124e-01
# 6 happiness     faculty   -0.02525431  0.0967258 -0.2610918 7.945753e-01
# 7    health (Intercept)   19.76489553  0.5412676 36.5159396 1.741411e-58
# 8    health         sex    0.32399380  0.6987735  0.4636607 6.439296e-01
# 9    health     faculty    0.10808545  0.3125010  0.3458723 7.301877e-01

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