给定分组数据的 Dataframe ：

library(tidyverse)

# fake up some grouped data: 
set.seed(123)
dat <- data.frame(x = rnorm(100), 
                  y = rnorm(100), 
                  group = rep(x=letters[1:10],each=10))
head(dat)
> head(dat)
            x           y group
1 -0.56047565 -0.71040656     a
2 -0.23017749  0.25688371     a
3  1.55870831 -0.24669188     a
4  0.07050839 -0.34754260     a
5  0.12928774 -0.95161857     a
6  1.71506499 -0.04502772     a

字符串
我想通过一个（或多个）分组列构建一组独立的模型：

# store models by group in a list
models <- list()
for(i in letters[1:10]) {
  models[[paste0("mdl_",i)]] = lm(y ~ x, dat %>% filter(group == i))
}

names(models)
 [1] "mdl_a" "mdl_b" "mdl_c" "mdl_d" "mdl_e" "mdl_f" "mdl_g" "mdl_h" "mdl_i" "mdl_j"

型
我可以通过多种方式将模型预测值（拟合值）添加到原始数据框中，这种方式很方便：

# add model predictions (fitted values) column to original data frame
dat <- dat %>%
  group_by(group) %>%
  mutate(fits = lm(y ~ x)$fitted.values)

# verify prediction from stored models and fitted values column match 
# to within a 10-decimal tolerance: 
for(i in letters[1:10]) {
  tmp <- dat %>%
    filter(group == i) %>%
    select(group, x, y, fits)
  tmp$stored_fit = predict(models[[paste0("mdl_",i)]], tmp)
  print(paste("mdl", i, "results match:", all(round(tmp$stored_fit,10) == round(tmp$fits,10))))
}
[1] "mdl a results match: TRUE"
[1] "mdl b results match: TRUE"
[1] "mdl c results match: TRUE"
[1] "mdl d results match: TRUE"
[1] "mdl e results match: TRUE"
[1] "mdl f results match: TRUE"
[1] "mdl g results match: TRUE"
[1] "mdl h results match: TRUE"
[1] "mdl i results match: TRUE"
[1] "mdl j results match: TRUE"

型
所有这些步骤都在其他问题中讨论过，比如this one。
现在，我想在一个新的data.frame上生成这些模型的预测，并将这些预测作为一个列添加到data. frame中。
以下是我尝试过的几件事：

# fake up some new grouped data: 
set.seed(456)
dat2 <- data.frame(x = rnorm(100), 
                   y = rnorm(100), 
                   group = rep(x=letters[1:10],each=10))

型

方法一（应用）：

tmp <- dat2 %>%
  group_by(group) %>%
  nest() # %>%
  # mutate(fits = map())

fits = as.data.frame(apply(X = tmp, MARGIN=1, FUN = function(X) predict(models[[paste0("mdl_",X$group)]], X$data)))
names(fits) = tmp$group
fits <- fits %>% 
  pivot_longer(cols = everything(), names_to = "group.fits") %>% 
  arrange(group.fits)

tmp <- tmp %>%
  unnest(cols = c(data)) %>%
  bind_cols(fits)

型
感觉很容易出错很不优雅

方法二（for loop，base r）：

tmp$fits = NA
for(g in unique(tmp$group)) {
  tmp[tmp$group==g,]$fits = predict(models[[paste0("mdl_",g)]], tmp[tmp$group==g,])
}
tmp

型
这没有什么特别的错误，除了循环在较大的数据集上是出了名的慢。

方法三（嵌套/Map）：

我以为下面这样的东西会起作用，但我在语法上有问题...

dat2 %>%
  group_by(group) %>%
  nest() %>%
  mutate(fits = map(.f = predict(models[[paste0("mdl_",group)]]), data))

或

mutate(fits = map(.x = data, 
                    .f = predict(models[[paste0("mdl_",group)]],
                                 .x)))

我正在寻找方法3的路线沿着的某个地方的答案--理想情况下，所有这些都在一组dplyr命令中。