我刚刚开始沉迷于编写函数,并使用lapply / purrr :: map()使我的代码更简洁,但显然还没有完全理解它。在当前示例中,我想重命名lm_robust对象的系数名称,然后更改lm_robust对象以合并新名称。我目前正在这样做:
library(dplyr)
library(purrr)
library(estimatr)
df <- tibble(interest = rnorm(1000), maturity = runif(1000, 1, 12), genderfemale = rbernoulli(1000),
y = 0.5*interest + 2*maturity - 3*genderfemale + rnorm(1000, sd = 0.25))
model1 <- lm_robust(y ~ interest + maturity + genderfemale, data = df, se_type = "stata")
model2 <- lm_robust(y ~ interest + I(interest^2) + maturity + genderfemale, data = df, se_type = "stata")
rename_coefficients <- function(x) {
x$term[which(x$term == "interest")] <- "Interest Rate"
x$term[which(x$term == "I(interest^2)")] <- "Interest Squared"
x$term[which(x$term == "maturity")] <- "Loan Maturity"
x$term[which(x$term == "genderfemaleTRUE")] <- "Female Borrower"
return(x$term)
}
temp <- map(list(model1, model2), rename_coefficients)
model1$term <- temp[[1]]
model2$term <- temp[[2]]
这是可行的,但是在我的用例中,我有更多的模型,并且首先将map()
的结果分配给temp,然后为每个模型包括部分model1$term <- temp[[1]]
困扰着我。< / p>
必须有一种更有效的方法吗?
答案 0 :(得分:1)
我们可以将两个步骤结合起来
purrr::map(list(model1, model2), ~{.x$term <- rename_coefficients(.x);.x})
#[[1]]
# Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
#(Intercept) -0.01957 0.020690 -0.9457 0.3445 -0.06017 0.02104 996
#Interest Rate 0.50310 0.008145 61.7719 0.0000 0.48712 0.51909 996
#Loan Maturity 2.00225 0.002563 781.3051 0.0000 1.99722 2.00728 996
#Female Borrower -2.97232 0.015790 -188.2375 0.0000 -3.00331 -2.94134 996
#[[2]]
# Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
#(Intercept) -0.016819 0.021597 -0.7787 0.4363 -0.05920 0.025563 995
#Interest Rate 0.502921 0.008105 62.0532 0.0000 0.48702 0.518825 995
#Interest Squared -0.002588 0.005618 -0.4606 0.6452 -0.01361 0.008436 995
#Loan Maturity 2.002219 0.002568 779.8058 0.0000 1.99718 2.007257 995
#Female Borrower -2.972270 0.015799 -188.1354 0.0000 -3.00327 -2.941268 995
这将返回更改了term
的模型背面列表。
或者使用lapply
lapply(list(model1, model2), function(x) {x$term <- rename_coefficients(x);x})