如何使用不同的自变量和因变量在 R 中添加标准化系数来运行多个线性回归？
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【中文标题】如何使用不同的自变量和因变量在 R 中添加标准化系数来运行多个线性回归？【英文标题】：How to run multiple linear regressions with different independent variables and dependent variables adding standardized coefficients in R? 【发布时间】：2019-10-04 20:55:19 【问题描述】：我目前正在尝试运行一个循环，对具有多个因变量 (n=1000) 的多个自变量 (n = 6) 执行线性回归。
这是一些示例数据，年龄、性别和教育程度代表我感兴趣的自变量，而 testcore_* 是我的因变量。
df = data.frame(ID = c(1001, 1002, 1003, 1004, 1005, 1006,1007, 1008, 1009, 1010, 1011),
age = as.numeric(c('56', '43','59','74','61','62','69','80','40','55','58')),
sex = as.numeric(c('0','1','0','0','1','1','0','1','0','1','0')),
testscore_1 = as.numeric(c('23','28','30','15','7','18','29','27','14','22','24')),
testscore_2 = as.numeric(c('1','3','2','5','8','2','5','6','7','8','2')),
testscore_3 = as.numeric(c('18','20','19','15','20','23','19','25','10','14','12')),
education = as.numeric(c('5','4','3','5','2', '1','4','4','3','5','2')))
我的工作代码允许我为多个 DV 运行回归模型（我敢肯定，更有经验的 R 用户会不喜欢它，因为它缺乏效率）：
y <- as.matrix(df[4:6])
#model for age
lm_results <- lm(y ~ age, data = df)
write.csv((broom::tidy(lm_results)), "lm_results_age.csv")
regression_results <-broom::tidy(lm_results)
standardized_coefficients <- lm.beta(lm_results)
age_standardize_results <- coef(standardized_coefficients)
write.csv(age_standardize_results, "lm_results_age_standardized_coefficients.csv")
然后，我将通过手动将 age 替换为 sex 和 education 来重复这一切
有没有人有更优雅的方式来运行这个 - 例如，通过所有感兴趣的 IV（即年龄、性别和教育）的循环？
如果有人建议将broom::tidy(lm_results) 与lm.beta::lm.beta 中的标准化系数结合起来，即将标准化回归系数与主模型输出相结合，我们将不胜感激。
【问题讨论】：
【参考方案1】：这是对我过去必须使用的类似工作流程的改编。记住要真正惩罚自己运行大量模型。我在您的数据框中添加了几个预测器列。祝你好运！！
解决方案：
# Creating pedictor and outcome vectors
ivs_vec <- names(df)[c(2:6, 10)]
dvs_vec <- names(df)[7:9]
# Creating formulas and running the models
ivs <- paste0(" ~ ", ivs_vec)
dvs_ivs <- unlist(lapply(ivs, function(x) paste0(dvs_vec, x)))
formulas <- lapply(dvs_ivs, formula)
lm_results <- lapply(formulas, function(x)
lm(x, data = df)
)
# Creating / combining results
tidy_results <- lapply(lm_results, broom::tidy)
dv_list <- lapply(as.list(stringi::stri_extract_first_words(dvs_ivs)), rep, 2)
tidy_results <- Map(cbind, dv_list, tidy_results)
standardized_results <- lapply(lm_results, function(x) coef(lm.beta::lm.beta(x)))
combined_results <- Map(cbind, tidy_results, standardized_results)
# Cleaning up final results
names(combined_results) <- dvs_ivs
combined_results <- lapply(combined_results, function(x) row.names(x) <- c(NULL); x)
new_names <- c("Outcome", "Term", "Estimate", "Std. Error", "Statistic", "P-value", "Standardized Estimate")
combined_results <- lapply(combined_results, setNames, new_names)
结果：
combined_results[1:5]
$`testscore_1 ~ age`
Outcome Term Estimate Std. Error Statistic P-value
Standardized Estimate
1 testscore_1 (Intercept) 18.06027731 12.3493569 1.4624468 0.1776424 0.00000000
2 testscore_1 age 0.05835152 0.2031295 0.2872627 0.7804155 0.09531823
$`testscore_2 ~ age`
Outcome Term Estimate Std. Error Statistic P-value Standardized Estimate
1 testscore_2 (Intercept) 3.63788676 4.39014570 0.8286483 0.4287311 0.0000000
2 testscore_2 age 0.01367313 0.07221171 0.1893478 0.8540216 0.0629906
$`testscore_3 ~ age`
Outcome Term Estimate Std. Error Statistic P-value Standardized Estimate
1 testscore_3 (Intercept) 6.1215175 6.698083 0.9139208 0.3845886 0.0000000
2 testscore_3 age 0.1943125 0.110174 1.7636870 0.1116119 0.5068026
$`testscore_1 ~ sex`
Outcome Term Estimate Std. Error Statistic P-value Standardized Estimate
1 testscore_1 (Intercept) 22.5 3.099283 7.2597435 4.766069e-05 0.0000000
2 testscore_1 sex -2.1 4.596980 -0.4568217 6.586248e-01 -0.1505386
$`testscore_2 ~ sex`
Outcome Term Estimate Std. Error Statistic P-value Standardized Estimate
1 testscore_2 (Intercept) 3.666667 1.041129 3.521816 0.006496884 0.0000000
2 testscore_2 sex 1.733333 1.544245 1.122447 0.290723029 0.3504247
数据：
df <- data.frame(ID = c(1001, 1002, 1003, 1004, 1005, 1006,1007, 1008, 1009, 1010, 1011),
age = as.numeric(c('56', '43','59','74','61','62','69','80','40','55','58')),
sex = as.numeric(c('0','1','0','0','1','1','0','1','0','1','0')),
pred1 = sample(1:11, 11),
pred2 = sample(1:11, 11),
pred3 = sample(1:11, 11),
testscore_1 = as.numeric(c('23','28','30','15','7','18','29','27','14','22','24')),
testscore_2 = as.numeric(c('1','3','2','5','8','2','5','6','7','8','2')),
testscore_3 = as.numeric(c('18','20','19','15','20','23','19','25','10','14','12')),
education = as.numeric(c('5','4','3','5','2', '1','4','4','3','5','2')))
【讨论】：
这太有用了！！谢谢@Andrew！ 这工作绝对漂亮！！！！非常感谢您的所有帮助，安德鲁！ 如果没有您的帮助，我们无法做到！再次感谢@Andrew。祝你有美好的一天！ 嗨@Andrew，再次感谢您在今年早些时候提供的所有帮助。我想在我的代码中感谢您，您希望我怎么做？ 你好@Andrew！我想知道您是否对我在此处发布的其他问题有建议：***.com/questions/60668630/… 它基于您在此答案中的代码。如果您有时间，将非常感谢您的建议！ :)【参考方案2】：一年后偶然发现了这一点，并记录了tidyverse 解决方案与@Andrew 相同的数据。
library(dplyr)
library(purrr)
library(tidyr)
library(stringi)
# Creating pedictor and outcome vectors
ivs_vec <- names(df)[c(2:6, 10)]
dvs_vec <- names(df)[7:9]
# Creating formulas and running the models
ivs <- paste0(" ~ ", ivs_vec)
dvs_ivs <- unlist(map(ivs, ~paste0(dvs_vec, .x)))
models <- map(setNames(dvs_ivs, dvs_ivs),
~ lm(formula = as.formula(.x),
data = df))
basics <-
map(models, ~ broom::tidy(.)) %>%
map2_df(.,
names(.),
~ mutate(.x, which_dependent = .y)) %>%
select(which_dependent, everything()) %>%
mutate(term = gsub("\\(Intercept\\)", "Intercept", term),
which_dependent = stringi::stri_extract_first_words(which_dependent))
basics$std_estimate <-
map_dfr(models, ~ coef(lm.beta::lm.beta(.)), .id = "which_dependent") %>%
pivot_longer(.,
cols = -which_dependent,
names_to = "term",
values_to = "std_estimate",
values_drop_na = TRUE) %>%
pull(std_estimate)
basics
#> # A tibble: 36 x 7
#> which_dependent term estimate std.error statistic p.value std_estimate
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 testscore_1 Intercept 18.1 12.3 1.46 0.178 0
#> 2 testscore_1 age 0.0584 0.203 0.287 0.780 0.0953
#> 3 testscore_2 Intercept 3.64 4.39 0.829 0.429 0
#> 4 testscore_2 age 0.0137 0.0722 0.189 0.854 0.0630
#> 5 testscore_3 Intercept 6.12 6.70 0.914 0.385 0
#> 6 testscore_3 age 0.194 0.110 1.76 0.112 0.507
#> 7 testscore_1 Intercept 22.5 3.10 7.26 0.0000477 0
#> 8 testscore_1 sex -2.10 4.60 -0.457 0.659 -0.151
#> 9 testscore_2 Intercept 3.67 1.04 3.52 0.00650 0
#> 10 testscore_2 sex 1.73 1.54 1.12 0.291 0.350
#> # … with 26 more rows
【讨论】：
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