r - 将 predict() 的结果放在列表内的 for 循环中

Question

假设我想在不同的样本上多次运行 mtcars 数据集上的线性回归模型。这个想法是，对于 for 循环中的每次迭代，每次对不同样本运行线性回归时，都存储 predict() 方法的结果。运行一次的小示例如下：

## Perform model once on a Sample and use model on full dataset:
Sample_Size <- 10
Sample <- mtcars[sample(nrow(mtcars), Sample_Size), ]
 Model <- lm(formula = mpg ~ wt, data = Sample)
 Predictions <- predict(Model,newdata=mtcars)
 ## Gets us a list with predicted wt for each car:
Predictions <- t(Predictions)

这产生

> Predictions
     Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout
[1,]  25.80494      23.89161   28.05592       21.34051          19.65228
       Valiant Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE
 [1,] 19.50221   18.67685  21.52809 21.82822 19.65228  19.65228   14.92523
     Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
 [1,]   17.47633    17.10117           6.071394            4.765828

 .... and so on for other cars

我想在 for 循环中多次执行此过程，每次选择不同的样本并获取对应的 Predictions() 列表，并将所有 Predictions() 结果逐行存储在数据框中。

假设我为两个不同的样本运行模型。结果数据帧的每一行都应该是该样本的上述结果，例如：

     Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout
 [1,]  25.80494      23.89161   28.05592       21.34051          19.65228
 [2,]  22.80492      22.89147   28.05532       21.34231          20.65290
       Valiant Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE
 [1,] 19.50221   18.67685  21.52809 21.82822 19.65228  19.65228   14.92523
 [2,] 21.83492   23.84147  29.02532 21.34231 20.35290  18.45228   13.92523

 ... and so on for other cars.

关于如何去做这件事的任何想法？我已经开发了一些东西，但它要么引发错误，要么只存储最后一个结果......我在这里错过了什么？

这是我到目前为止所拥有的：

### Inside a for loop, to get a dataframe of Predictions:

Bootstrap_times <- 2
Sample_Size <- 10
Predictions <- list()
Results <-vector ("list",Bootstrap_times)## Stores the Predictions for each run

for(i in 1:Bootstrap_times){
### Take a sample
Sample[[i]] <- mtcars[sample(nrow(mtcars), Sample_Size), ]
### Do the regression on the sample
Model[[i]] <- lm(formula = mpg ~ wt, data = Sample[[i]])
### Perform the predict() on the sample
Predictions[[i]] <- predict(Model[[i]],newdata=mtcars)
### put the result as a line on the dataframe Results
Predictions[[i]] <- t(Predictions[[i]])
return(Predictions)
}

但是，我不断得到：

[[<-.data.frame( , i, value = list( mpg *tmp*= c(13.3, 10.4, : 替换有 10 行，数据有 0

标签： rfor-looplinear-regression

Answer 1

我更喜欢使用，magic_for()但是你也可以很容易地使用 base R 来做到这一点。

这是一个例子：

Bootstrap_times <- 2
Sample_Size     <- 10

Sample      <- mtcars[sample(nrow(mtcars), Sample_Size), ]
Model       <- lm(formula = mpg ~ wt, data = Sample)
Predictions <- predict(Model,newdata=mtcars)
## You like how I line up arrows, right?
Predictions <- t(Predictions)


Predictions <- list()
Results     <-vector ("list",Bootstrap_times)## Stores the Predictions for each run

magicfor::magic_for()
for(i in 1:Bootstrap_times){
  ### Take a sample
  Sample[[i]] <- mtcars[sample(nrow(mtcars), Sample_Size), ]
  ### Do the regression on the sample
  Model[[i]] <- lm(formula = mpg ~ wt, data = Sample[[i]])
  ### Perform the predict() on the sample

  put(predict(Model[[i]],newdata=mtcars))
}

tmp<-magicfor::magic_result_as_dataframe()

tmp

   i predict(Model[[i]],newdata=mtcars)
1  1                          22.858806
2  2                          20.922763
3  1                          25.136504
4  2                          18.341372
5  1                          16.633098
6  2                          16.481252
7  1                          15.646096
8  2                          18.531180
9  1                          18.834873
10 2                          16.633098
11 1                          16.633098
12 2                          11.849933
13 1                          14.431324
14 2                          14.051708
15 1                           2.890988
16 2                           1.569924
17 1                           2.169717
18 2                          26.047583
19 1                          30.489093
20 2                          28.818782
21 1                          24.035616
22 2                          16.025712
23 1                          16.671060
24 2                          13.596168
25 1                          13.558206
26 2                          28.059549
27 1                          26.503122
28 2                          31.263511
29 1                          18.683026
30 2                          21.719957
31 1                          15.646096
32 2                          21.644034
33 1                          22.978374
34 2                          21.584264
35 1                          24.618503
36 2                          19.725450
37 1                          18.495353
38 2                          18.386011
39 1                          17.784630
40 2                          19.862128
41 1                          20.080812
42 2                          18.495353
43 1                          18.495353
44 2                          15.051081
45 1                          16.909894
46 2                          16.636540
47 1                           8.599905
48 2                           7.648629
49 1                           8.080530
50 2                          25.274555
51 1                          28.472808
52 2                          27.270046
53 1                          23.825774
54 2                          18.057985
55 1                          18.522689
56 2                          16.308514
57 1                          16.281178
58 2                          26.723336
59 1                          25.602581
60 2                          29.030452
61 1                          19.971470
62 2                          22.158309
63 1                          17.784630
64 2                          22.103638