查找 grouped_by 线性模型的预测

Question

我想根据适合训练数据集的模型获得预测值。我以前做过这个，但现在我有一个分组因素，它让我失望了。我想根据每个环境的人口预测生物量。

library(tidyverse)

fit_mods<-df %>%
  group_by(environ) %>%
  do(model = lm(biomass ~ poly(population, 2), data = .))

最终，我会想找到最大的种群生物量。通常我会通过创建一个网格和运行基于我的新值的模型并找到最大值来做到这一点，但我不知道如何通过分组来做到这一点。通常的方式：

min_pop <- min(df$population)
max_pop <- max(df$population)

grid_pop <- expand.grid(new = (seq(from = min_pop,
                                   to = max_pop, 
                                   length.out = 1000)),
                        environ = c("A", "B"))  

 #This is what I did with ungrouped data, but doesn't work now.
 pred_pop <- predict(object = fit_mods, 
                newdata = grid_pop,
                interval = "predict")

这是一些虚拟数据：

  df <- as.data.frame(list(environ = c("a", "a", "a", "a", "a", "b", "b", "b", "b", "b"),
   population = c(2, 3, 4, 5, 6, 3, 4, 5, 6, 7), 
   biomass = c(1, 2.2, 3.5, 4.1, 3.8, 2.5, 3.6, 4.3, 5.2, 5.1)), class = "data.frame")

Answer 1

在 tidyverse many models 方法中，您可以按以下方式进行：

library(tidyverse)

   fit_mods <- df %>%
     nest(-environ) %>% 
     mutate(models = map(data, ~ lm(biomass ~ poly(population, 2), data = .x)),
         min_pop = map_dbl(data, ~ pull(.x, population) %>% min),
         max_pop = map_dbl(data, ~ pull(.x, population) %>% max),
         new = map2(min_pop, max_pop, ~ tibble(population = seq(from = .x,
                                                to = .y, 
                                                length.out = 1000))),
         pred = map2(models,
                     new,
                     ~ predict(object = .x,
                               newdata = select(.y,population),
                               interval = "predict")))

查找 grouped_by 线性模型的预测

Find predictions for linear model that is grouped_by

r

dplyr

broom