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使用group by和tidy运行多个模型并将结果提取到dataframe

data-manipulation linear-regression r

aelhak · 技术社区 · 6 年前

我想使用 group_by %>% do(tidy(*)) 运行多个线性回归模型,并将模型结果提取到数据框中。每个模型的数据框架应包括以下内容:结果变量、暴露变量、样本量、β系数、SE和p值。

library(tidyverse)
data("mtcars")
outcomes <- c("wt, mpg", "hp", "disp")
exposures <- c("gear", "vs", "am")
covariates <- c("drat", "qsec")

模型应回归针对所有协变量调整的每次暴露的每个结果,例如:。

lm(wt ~ factor(gear)+drat+qsec, mtcars, na.action = na.omit)
lm(wt ~ factor(vs)+drat+qsec, mtcars, na.action = na.omit)
etc...

最终的代码可能是这样的?

models <- (mtcars %>%
gather(x_var, x_value, -c(y_var, y_i, cv1:cv3)) %>%
group_by(y_var, x_var) %>%
do(broom::tidy(lm(y_i ~ x_value + cv1 + cv2 + cv3, data = .))))

1 回复 | 直到 6 年前

AntoniosK 6 年前

以下是一个解决方案,它首先为要运行的每个模型创建公式,然后从要分析的数据集中调用正确的变量,而不是重塑数据集本身并应用模型:

library(tidyverse)
library(broom)

outcomes <- c("wt", "mpg", "hp", "disp")
exposures <- c("gear", "vs", "am")
covariates <- c("drat", "qsec")

expand.grid(outcomes, exposures, covariates) %>%
  group_by(Var1, Var2) %>%
  summarise(Var3 = paste0(Var3, collapse = "+")) %>%
  rowwise() %>%
  summarise(frm = paste0(Var1, "~factor(", Var2, ")+", Var3)) %>%
  group_by(model_id = row_number(),
           frm) %>%
  do(tidy(lm(.$frm, data = mtcars))) %>%
  ungroup()

# # A tibble: 52 x 7
#   model_id frm                       term          estimate std.error statistic     p.value
#      <int> <chr>                     <chr>            <dbl>     <dbl>     <dbl>       <dbl>
# 1        1 wt~factor(gear)+drat+qsec (Intercept)      9.25     2.17       4.27  0.000218   
# 2        1 wt~factor(gear)+drat+qsec factor(gear)4   -0.187    0.493     -0.378 0.708      
# 3        1 wt~factor(gear)+drat+qsec factor(gear)5   -0.703    0.518     -1.36  0.186      
# 4        1 wt~factor(gear)+drat+qsec drat            -1.03     0.425     -2.42  0.0227     
# 5        1 wt~factor(gear)+drat+qsec qsec            -0.121    0.0912    -1.32  0.196      
# 6        2 wt~factor(vs)+drat+qsec   (Intercept)      4.35     2.28       1.91  0.0663     
# 7        2 wt~factor(vs)+drat+qsec   factor(vs)1     -1.04     0.416     -2.49  0.0189     
# 8        2 wt~factor(vs)+drat+qsec   drat            -0.918    0.263     -3.49  0.00160    
# 9        2 wt~factor(vs)+drat+qsec   qsec             0.147    0.106      1.39  0.175      
# 10        3 wt~factor(am)+drat+qsec   (Intercept)      8.29     1.31       6.33  0.000000766
# # ... with 42 more rows

如果您喜欢使用 map 从…起 purrr 包而不是 do :

expand.grid(outcomes, exposures, covariates) %>%
  group_by(Var1, Var2) %>%
  summarise(Var3 = paste0(Var3, collapse = "+")) %>%
  rowwise() %>%
  summarise(frm = paste0(Var1, "~factor(", Var2, ")+", Var3)) %>%
  group_by(model_id = row_number()) %>%
  mutate(model = map(frm, ~tidy(lm(., data = mtcars)))) %>%
  unnest() %>%
  ungroup()

记住,这种方法的关键是创建公式。因此,如果您能够以稍微不同的方式指定变量,并帮助创建代码更少的公式,代码就会变得更简单:

outcomes <- c("wt", "mpg", "hp", "disp")
exposures <- c("gear", "vs", "am")
covariate1 <- "drat"
covariate2 <- "qsec"

expand.grid(outcomes, exposures, covariate1, covariate2) %>%
  transmute(frm = paste0(Var1, "~factor(", Var2, ")+", Var3, "+", Var4)) %>%
  group_by(model_id = row_number()) %>%
  mutate(model = map(frm, ~tidy(lm(., data = mtcars)))) %>%
  unnest() %>%
  ungroup()