从 R 中的多个简单线性回归中获取摘要

Question

我正在对我的数据集中的多个组执行简单的线性回归。但是，我想从这些回归中提取摘要，并按组将它们放入主表中。我可以像这样运行它（它可以工作）：

  fit_basic <- rs2_anova %>% #Run multiple simple linear regressions
    group_by(quant_method) %>% 
    nest() %>% 
    mutate(model = map(data, ~lm(recoveries ~ treatment, data = .))) 

fit_basic_A <- fit_basic[[1,"model"]] #Remove the model from fit_basic
fit_basic_B <- fit_basic[[1,"model"]] #Remove the model from fit_basic

fit_basic_table_A <- get_regression_table(fit_basic_A) %>%
  select("term", "estimate") %>%
  pivot_wider(names_from = "term", values_from = "estimate") %>%
  mutate(quant_method = "A")

fit_basic_table_B <- get_regression_table(fit_basic_A) %>%
  select("term", "estimate") %>%
  pivot_wider(names_from = "term", values_from = "estimate") %>%
  mutate(quant_method = "B")

fit_basic_table <- rbind(fit_basic_table_A, fit_basic_table_B)

为了节省几行代码（因为我的组比这里介绍的多得多），我想我可以使用 map 函数，但我一直卡在映射汇总表上，这会引发错误：

fit_basic <- rs2_anova %>% 
  group_by(quant_method) %>% 
  nest() %>% 
  mutate(model = map(data, ~lm(recoveries ~ treatment, data = .))) %>%
  mutate(summaries = map(data, get_regression_table(.$model)))

Error in input_checks(model, digits, print) : 
  Only simple linear regression models are supported. Try again using only `lm()` models as appropriate.

我也尝试过类似的方法：

fit_basic_table <- map(fit_basic$model, 
                           function(x) {
                             p <- get_regression_table(x)
                             cbind(par=rownames(p), p)
                           }) 

但是我得到了一个无法分解为单个数据框的数据框列表，并且我丢失了我的组指定。我试过了：

fit_basic_table <- map(fit_basic$model, 
                           function(x) {
                             p <- get_regression_table(x)
                             cbind(par=rownames(p), p)
                           }) %>% 
  map_df(as_tibble, .id = "id") 

和

fit_basic_table <- map(fit_basic$model, 
                           function(x) {
                             p <- get_regression_table(x)
                             cbind(par=rownames(p), p)
                           }) %>% 
  unnest(cols = "id")

关于如何实现自动化的任何想法？

*随机测试数据框：

quant_method <- c("A", "A","A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B","B","B")
treatment <- c("x","x","x","x","x","y","y","y","y","y","x","x","x","x","x","y","y","y","y","y")
recoveries <-c("88","86","87","82","85","76","65","55","72","71","98","96","97","92","99","66",
               "55","55","62","61")
rs2_anova <- data.frame(quant_method, treatment, recoveries)

Answer 1

这是使用tidyverse 和broom 包的一种解决方案。它与您尝试的 purrr 方法略有不同，但我认为结果显示了您有兴趣从 lm 对象中提取的术语（即、术语和估计）。

library(tidyverse)
library(broom)

#Added the stringsAsFactors argument = F to avoid an error in the lm model
rs2_anova <- data.frame(quant_method, 
                        treatment, 
                        recoveries,
                        stringsAsFactors = F)

fit_basic <- rs2_anova %>% 
  #Group by quant_method column
  group_by(quant_method) %>%
  #do the linear models by grouping var
  do(model = lm(recoveries ~ treatment, data = .)) %>%
  #tidy lm object and order it as tibble
  tidy(model)

Answer 2

我在这里找到了答案：unnest a list column after modeling after group_by in r

并修改为：

fit_cec <- rs2_anova %>% 
  group_by(quant_method) %>%
  nest %>% 
  mutate(data = map(data, ~ .x %>%
                      summarise(model = list(broom::tidy(lm(recoveries ~ treatment)))))) %>% 
  unnest(data) %>% 
  unnest(model)

或获取所有估计、预测和摘要 (https://drsimonj.svbtle.com/running-a-model-on-separate-groups)。这也很好用：

fit_cec <- rs2_anova %>% 
  group_by(quant_method) %>%
  nest %>% 
  mutate(fit = map(data, ~ lm(loss_abs_BC_2 ~ cec, data = .)),
         parameters = map(fit, tidy), #provides estimate table for slope and y-intercept with std.error and estimate p-values
         summary = map(fit, broom::glance), #provides R2, adj.R2, sigma, and model p-value
         predictions = map(fit, augment)) %>% #provides fitted values with residuals and errors
  unnest(parameters) %>%
  pivot_wider(names_from = term, values_from = c(estimate, std.error, statistic, p.value)) %>%
  unnest(summary) %>%
  unnest(predictions)