如何通过向量化获得多元回归的公式

Question

假设我有以下代码进行多重回归并将 lm 和 lm 与逐步选择模型存储在 tibbles 中：

library(dplyr)
library(tibble)
library(MASS)
set.seed(1)

df <- data.frame(A = sample(3, 10, replace = T), 
                 B = sample(100, 10, replace = T), 
                 C = sample(100, 10, replace = T))
df <- df %>% arrange(A)

formula_df <- as.tibble(NA)
aic_df <- as.tibble(NA)

for (i in unique(df$A)){
    temp <- df %>% filter(A == i)

    formula_df[i, 1] <- temp %>% 
        do(model = lm(B ~ C, data = .))

    aic_df[i, 1] <- temp %>%
        do(model = stepAIC(formula_df[[1,1]], direction = "both", trace = F))
}

是否可以矢量化以使其更快，例如使用 *pply 函数？当数据变大时，循环变得非常慢。提前谢谢你。

Answer 1

您可以尝试类似的方法：

model <- df %>% group_by(A) %>% 
    summarise(formula_model = list(lm(B ~ C))) %>% 
    mutate(aic_model = list(stepAIC(.[[1,2]], direction = "both", trace = F)))

如何通过向量化获得多元回归的公式

How to get formulas of multiple regressions by vectorizing

r

vectorization

lm