r滤波器和汇总结果来自lapply模型摘要

发布于 2025-01-29 16:43:30 字数 481 浏览 4 评论 0原文

我正在尝试使用dlply在数据集的子集上执行的多个回归模型过滤和汇总结果。

这就是我的模型的方式：

library(plyr)

data("mtcars")

models = dlply(mtcars, .(cyl), function(df) lm(mpg ~ hp,data=df))
lapply(models, summary)

现在，我将类似不同模型的结果（圆柱体4、6、8）结合在一起：

rbind(
  c("Cylinder 4", coef(lapply(models, summary)$`4`)[2,]),
  c("Cylinder 6", coef(lapply(models, summary)$`6`)[2,]),
  c("Cylinder 8", coef(lapply(models, summary)$`8`)[2,])
)

是否有一种方法可以更有效地总结这一点？

原文

I am trying to filter and aggregate results from multiple regression models executed on a subset of dataset using dlply.

This is how I ran my models:

library(plyr)

data("mtcars")

models = dlply(mtcars, .(cyl), function(df) lm(mpg ~ hp,data=df))
lapply(models, summary)

Right now I am combining the results from different models(cylinder 4, 6, 8) like this:

rbind(
  c("Cylinder 4", coef(lapply(models, summary)I am trying to filter and aggregate results from multiple regression models executed on a subset of dataset using dlply.
This is how I ran my models:
library(plyr)

data("mtcars")

models = dlply(mtcars, .(cyl), function(df) lm(mpg ~ hp,data=df))
lapply(models, summary)

Right now I am combining the results from different models(cylinder 4, 6, 8) like this:
4`)[2,]),
  c("Cylinder 6", coef(lapply(models, summary)I am trying to filter and aggregate results from multiple regression models executed on a subset of dataset using dlply.
This is how I ran my models:
library(plyr)

data("mtcars")

models = dlply(mtcars, .(cyl), function(df) lm(mpg ~ hp,data=df))
lapply(models, summary)

Right now I am combining the results from different models(cylinder 4, 6, 8) like this:
6`)[2,]),
  c("Cylinder 8", coef(lapply(models, summary)I am trying to filter and aggregate results from multiple regression models executed on a subset of dataset using dlply.
This is how I ran my models:
library(plyr)

data("mtcars")

models = dlply(mtcars, .(cyl), function(df) lm(mpg ~ hp,data=df))
lapply(models, summary)

Right now I am combining the results from different models(cylinder 4, 6, 8) like this:
8`)[2,])
)

Is there a way to summarize this more efficiently?

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抱猫软卧 2025-02-05 16:43:30

我们可以从broom中使用整理，而不是使用摘要和coef。我们还可以将模型数据直接输送到map2_df。

library(tidyverse)

dlply(mtcars, .(cyl), function(df)
  lm(mpg ~ hp, data = df)) %>%
  map2_df(
    .,
    names(.),
    ~ tidy(.x)[2,] %>% mutate(Cylinder = paste0("Cylinder ", .y)) %>% tibble::column_to_rownames("Cylinder")
  )

输出

  term  estimate std.error statistic p.value Cylinder
  <chr>    <dbl>     <dbl>     <dbl>   <dbl> <chr>   
1 hp    -0.113      0.0612    -1.84   0.0984 4       
2 hp    -0.00761    0.0266    -0.286  0.786  6       
3 hp    -0.0142     0.0139    -1.02   0.326  8

We can use tidy from broom, rather than using summary and coef. We can also just pipe the model data straight into map2_df.

library(tidyverse)

dlply(mtcars, .(cyl), function(df)
  lm(mpg ~ hp, data = df)) %>%
  map2_df(
    .,
    names(.),
    ~ tidy(.x)[2,] %>% mutate(Cylinder = paste0("Cylinder ", .y)) %>% tibble::column_to_rownames("Cylinder")
  )

Output

  term  estimate std.error statistic p.value Cylinder
  <chr>    <dbl>     <dbl>     <dbl>   <dbl> <chr>   
1 hp    -0.113      0.0612    -1.84   0.0984 4       
2 hp    -0.00761    0.0266    -0.286  0.786  6       
3 hp    -0.0142     0.0139    -1.02   0.326  8

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