How can I modify these dplyr code for multiple linear regression by combination of all variables in R

问题

lets say I have following data

ind1 <- rnorm(99)
ind2 <- rnorm(99)
ind3 <- rnorm(99)
ind4 <- rnorm(99)
ind5 <- rnorm(99)
dep <- rnorm(99, mean=ind1)
group <- rep(c("A", "B", "C"), each=33)
df <- data.frame(dep,group, ind1, ind2, ind3, ind4, ind5)

the following code is calculating multiple linear regression between dependend variable and 2 independent variables by group which is exactly what I want to do. But I want to regress dep variable against all combination pair of independent variables at once. So how can I combine other models in this code?

df %>% 
  nest(-group) %>% 
  mutate(fit = map(data, ~ lm(dep ~ ind1 + ind2, data = .)),
         results1 = map(fit, glance),
         results2 = map(fit, tidy)) %>% 
  unnest(results1) %>% 
  unnest(results2) %>% 
  select(group, term, estimate, r.squared, p.value, AIC) %>% 
  mutate(estimate = exp(estimate))

Thanks in advance!

回答1:

Not a full tidy answer. Consider building all possible combinations of linear formulas with rapply after initial build with lapply and combn then pass into your tidy method:

indvar_list <- lapply(1:5, function(x) 
                 combn(paste0("ind", 1:5), x, , simplify = FALSE))

formulas_list <- rapply(indvar_list, function(x)
                   as.formula(paste("dep ~", paste(x, collapse="+"))))

run_model <- function(f) {    
    df %>% 
      nest(-group) %>% 
      mutate(fit = map(data, ~ lm(f, data = .)),
             results1 = map(fit, glance),
             results2 = map(fit, tidy)) %>% 
      unnest(results1) %>% 
      unnest(results2) %>% 
      select(group, term, estimate, r.squared, p.value, AIC) %>% 
      mutate(estimate = exp(estimate))
}

tibble_list <- lapply(formulas_list, run_model)

来源：https://stackoverflow.com/questions/56093202/how-can-i-modify-these-dplyr-code-for-multiple-linear-regression-by-combination

标签

group-by

dplyr

linear-regression

tidyverse