从适合 purrr 的模型中提取残差
Extract residuals from models fit in purrr
我对我的数据进行了分组,并为每组拟合了一个模型,我希望得到每组的残差。我可以使用 RStudio 的查看器查看每个模型的残差,但我不知道如何提取它们。提取一组残差可以像 diamond_mods[[3]][[1]][["residuals"]]
那样完成,但是我如何使用 purrr 从每个组中提取残差集(连同扫帚以得到一个漂亮的 tibble)?
以下是我的进度:
library(tidyverse)
library(purrr)
library(broom)
fit_mod <- function(df) {
lm(price ~ poly(carat, 2, raw = TRUE), data = df)
}
diamond_mods <- diamonds %>%
group_by(cut) %>%
nest() %>%
mutate(
model = map(data, fit_mod),
tidied = map(model, tidy)
#resid = map_dbl(model, "residuals") #this was my best try, it doesn't work
) %>%
unnest(tidied)
你很接近 - 但你应该使用 map()
而不是 map_dbl()
因为你需要 return 列表而不是向量。
diamond_mods <- diamonds %>%
group_by(cut) %>%
nest() %>%
mutate(
model = map(data, fit_mod),
tidied = map(model, tidy),
resid = map(model, residuals)
)
使用 devel
版本的 dplyr
,我们可以在按 'cut'
分组后在 condense
中执行此操作
library(dplyr)
library(ggplot2)
library(broom)
diamonds %>%
group_by(cut) %>%
condense(model = fit_mod(cur_data()),
tidied = tidy(model),
resid = model[["residuals"]])
# A tibble: 5 x 4
# Rowwise: cut
# cut model tidied resid
# <ord> <list> <list> <list>
#1 Fair <lm> <tibble [3 × 5]> <dbl [1,610]>
#2 Good <lm> <tibble [3 × 5]> <dbl [4,906]>
#3 Very Good <lm> <tibble [3 × 5]> <dbl [12,082]>
#4 Premium <lm> <tibble [3 × 5]> <dbl [13,791]>
#5 Ideal <lm> <tibble [3 × 5]> <dbl [21,551]>
我对我的数据进行了分组,并为每组拟合了一个模型,我希望得到每组的残差。我可以使用 RStudio 的查看器查看每个模型的残差,但我不知道如何提取它们。提取一组残差可以像 diamond_mods[[3]][[1]][["residuals"]]
那样完成,但是我如何使用 purrr 从每个组中提取残差集(连同扫帚以得到一个漂亮的 tibble)?
以下是我的进度:
library(tidyverse)
library(purrr)
library(broom)
fit_mod <- function(df) {
lm(price ~ poly(carat, 2, raw = TRUE), data = df)
}
diamond_mods <- diamonds %>%
group_by(cut) %>%
nest() %>%
mutate(
model = map(data, fit_mod),
tidied = map(model, tidy)
#resid = map_dbl(model, "residuals") #this was my best try, it doesn't work
) %>%
unnest(tidied)
你很接近 - 但你应该使用 map()
而不是 map_dbl()
因为你需要 return 列表而不是向量。
diamond_mods <- diamonds %>%
group_by(cut) %>%
nest() %>%
mutate(
model = map(data, fit_mod),
tidied = map(model, tidy),
resid = map(model, residuals)
)
使用 devel
版本的 dplyr
,我们可以在按 'cut'
condense
中执行此操作
library(dplyr)
library(ggplot2)
library(broom)
diamonds %>%
group_by(cut) %>%
condense(model = fit_mod(cur_data()),
tidied = tidy(model),
resid = model[["residuals"]])
# A tibble: 5 x 4
# Rowwise: cut
# cut model tidied resid
# <ord> <list> <list> <list>
#1 Fair <lm> <tibble [3 × 5]> <dbl [1,610]>
#2 Good <lm> <tibble [3 × 5]> <dbl [4,906]>
#3 Very Good <lm> <tibble [3 × 5]> <dbl [12,082]>
#4 Premium <lm> <tibble [3 × 5]> <dbl [13,791]>
#5 Ideal <lm> <tibble [3 × 5]> <dbl [21,551]>