R-向观测矩阵添加外推（lm）值

我正在尝试向R中的矩阵添加一组外推的“观察值”。我知道如何使用常规编程技术（读取；一堆嵌套的循环和函数）来实现这一点，但我觉得通过使用内置的R功能，必须以更干净的方式实现这一点

下面的代码说明了这一点，以及它的故障点

非常感谢您的帮助

致以亲切的问候

西尔万

library(dplyr)

# The idea is that i have a table of observations for e.g. x=5, 6, 7, 8, 9 and 10. The observations (in this example 2)
# conform fairly decently to sets of 2nd order polynomials.
# Now, I want to add an extrapolated value to this table (e.g. x=4). I know how to do this programmically 
# but I feel there must be a cleaner solution to do this. 

#generate dummy data table
x <- 5:10
myData <- tibble(x, a = x^2 * 2 + x * 3 + 4 + rnorm(1,0,0.01), b = x^2 * 3 + x * 4 + 5 + rnorm(1,0,0.01)   )

#Gather (put in Data-Key format)
myDataKeyFormat <- gather(myData,key = "someLabel", value = "myObservation", -x)
fitted_models <- myDataKeyFormat %>% group_by(someLabel) %>% do(model = lm(myObservation ~ poly(x,2), data = .))
myExtrapolatedDataPointx <- tibble(x = 4)

#Add the x=4 field
fitted_points <- fitted_models %>% group_by(someLabel) %>% do(predict(.$model,myExtrapolatedDataPointx)) #R really doesnt like this bit

#append the fitted_points to the myDataKeyFormat
myDataKeyFormatWithExtrapolation <- union(myDataKeyFormat,fitted_points)

#use spread to 
myDataWithExtrapolation <- myDataKeyFormatWithExtrapolation %>% spread(someLabel,myObservation)

库（dplyr）
#我的想法是，我有一个观察表，例如x=5、6、7、8、9和10。观察结果（在本例2中）
#完全符合二阶多项式的集合。
#现在，我想给这个表添加一个外推值（例如x=4）。我知道如何通过编程实现这一点
#但我觉得必须有一个更干净的解决方案来做到这一点。
#生成虚拟数据表
x这是tidyverse中的一个解决方案，使用
purr
创建不同的模型。其思想是嵌套（使用
tidyr:：nest
），然后
purr:：map
来训练模型。然后，我将添加新值并使用
modeler:：add_predictions
计算预测。在这里，所有的数据都在同一个地方：训练数据、模型、测试数据和预测，通过变量
someLabel
。我还提供了一种可视化数据的方法。
您可以查看Hadley Wickham&Garrett Grolemund，尤其是关于模型的部分，以了解更多信息

library(dplyr)
library(tibble)
library(tidyr)
library(purrr)
library(modelr)
library(ggplot2)

set.seed(1) # For reproducibility
x <- 5:10
myData <- tibble(x, 
                 a = x^2 * 2 + x * 3 + 4 + rnorm(1,0,0.01), 
                 b = x^2 * 3 + x * 4 + 5 + rnorm(1,0,0.01))

#Gather (put in Data-Key format)
myDataKeyFormat <- gather(myData,key = "someLabel", value = "myObservation", -x)

myModels <- myDataKeyFormat %>% 
  nest(-someLabel) %>% 
  mutate(model = map(data, ~lm(myObservation ~ poly(x,2), data = .x)))

我添加预测：
add_predictions
将数据帧和模型作为参数，因此我使用
map2
映射新数据和模型

fitted_models <- new_data %>% 
  mutate(new = map2(new, model, ~add_predictions(.x, .y)))
fitted_models
# A tibble: 2 × 4
  someLabel             data    model              new
      <chr>           <list>   <list>           <list>
1         a <tibble [6 × 2]> <S3: lm> <tibble [4 × 2]>
2         b <tibble [6 × 2]> <S3: lm> <tibble [4 × 2]>

非常感谢@FlorianGD，这正是我想要做的！
# New data
new_data <- myModels %>% 
  mutate(new = map(data, ~tibble(x = c(3, 4, 11, 12))))

fitted_models <- new_data %>% 
  mutate(new = map2(new, model, ~add_predictions(.x, .y)))
fitted_models
# A tibble: 2 × 4
  someLabel             data    model              new
      <chr>           <list>   <list>           <list>
1         a <tibble [6 × 2]> <S3: lm> <tibble [4 × 2]>
2         b <tibble [6 × 2]> <S3: lm> <tibble [4 × 2]>

my_points <- bind_rows(unnest(fitted_models, data),
          unnest(fitted_models, new))

ggplot(my_points)+
  geom_point(aes(x = x, y = myObservation), color = "black") +
  geom_point(aes(x = x, y = pred), color = "red")+
  facet_wrap(~someLabel)