控制R中pmap输出的顺序答案

【问题标题】：Control the order of pmap output in R控制R中pmap输出的顺序
【发布时间】：2021-11-11 00:16:39
【问题描述】：

我有很多统计模型要运行，我正在尝试使用 pmap 循环遍历每个模型所需的变量名称。我想返回模型摘要以及有关每个模型的额外信息。我能够运行模型并返回模型摘要和额外信息，但不是将额外信息和模型摘要放在一起，而是输出首先返回所有额外信息，然后返回所有模型摘要。每次迭代是否可以将额外信息和模型摘要一起返回？下面我有一个简化的例子。


    #Import library
    library(purrr)
    
    #Set up vars
    y1 <- c(runif(20, 0, 1))
    y2 <- c(runif(20, 0, 1))
    x1 <- c(rnorm(20, 0, 1))
    x2 <- c(rnorm(20, 0, 1))
    
    #Collect vars in lists
    ys <- list(y1, y2)
    xs <- list(x1, x2)
    
    #Write function with a model and "extra information"
    regressor <- function(y, x){
      #extra information
      mean_y <- mean(y) 
      cat("data:", mean_y, "\n\n")
      
      #model
      model <- lm(y ~ x)
      summary(model)
    }
    
    #Use pmap to run model over the vars
    pmap(list(ys, xs), regressor)

当我运行上面的代码时，我的输出如下所示：

>data: 0.5281057 
>
>data: 0.5522678 
>
>[[1]]
>
>Call:
>lm(formula = y ~ x)
>
>Residuals:
>     Min       1Q   Median       3Q      Max 
>-0.57284 -0.24802  0.03689  0.26913  0.47428 
>
>Coefficients:
>            Estimate Std. Error t value Pr(>|t|)    
>(Intercept)  0.54894    0.07203   7.621 4.86e-07 ***
>x           -0.12909    0.07718  -1.673    0.112    
>---
>Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>Residual standard error: 0.3173 on 18 degrees of freedom
>Multiple R-squared:  0.1345,   Adjusted R-squared:  0.08643 
>F-statistic: 2.797 on 1 and 18 DF,  p-value: 0.1117
>
>
>[[2]]
>
>Call:
>lm(formula = y ~ x)
>
>Residuals:
>     Min       1Q   Median       3Q      Max 
>-0.49107 -0.18591 -0.05057  0.27710  0.50679 
>
>Coefficients:
>            Estimate Std. Error t value Pr(>|t|)    
>(Intercept)  0.54197    0.06764   8.013  2.4e-07 ***
>x           -0.05526    0.06441  -0.858    0.402    
>---
>Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>Residual standard error: 0.2977 on 18 degrees of freedom
>Multiple R-squared:  0.03928,  Adjusted R-squared:  -0.01409 
>F-statistic: 0.736 on 1 and 18 DF,  p-value: 0.4022

我希望结果看起来像这样：

>[[1]]
>
>data: 0.5281057 
>
>Call:
>lm(formula = y ~ x)
>
>Residuals:
>     Min       1Q   Median       3Q      Max 
>-0.57284 -0.24802  0.03689  0.26913  0.47428 
>
>Coefficients:
>            Estimate Std. Error t value Pr(>|t|)    
>(Intercept)  0.54894    0.07203   7.621 4.86e-07 ***
>x           -0.12909    0.07718  -1.673    0.112    
>---
>Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>Residual standard error: 0.3173 on 18 degrees of freedom
>Multiple R-squared:  0.1345,   Adjusted R-squared:  0.08643 
>F-statistic: 2.797 on 1 and 18 DF,  p-value: 0.1117
>
>
>[[2]]
>
>data: 0.5522678 
>
>Call:
>lm(formula = y ~ x)
>
>Residuals:
>     Min       1Q   Median       3Q      Max 
>-0.49107 -0.18591 -0.05057  0.27710  0.50679 
>
>Coefficients:
>            Estimate Std. Error t value Pr(>|t|)    
>(Intercept)  0.54197    0.06764   8.013  2.4e-07 ***
>x           -0.05526    0.06441  -0.858    0.402    
>---
>Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>Residual standard error: 0.2977 on 18 degrees of freedom
>Multiple R-squared:  0.03928,  Adjusted R-squared:  -0.01409 
>F-statistic: 0.736 on 1 and 18 DF,  p-value: 0.4022

【问题讨论】：

您确实意识到在将数据打印/cat 到控制台时会返回结果。除非您返回数据，否则这在任何语言中几乎都是不可能的。更改您的函数以返回 list(data = mean_y, summary = summary(model))

标签： r tidyverse purrr pmap

【解决方案1】：

让输出一个块

regressor <- function(y, x){
  #extra information
  mean_y <- mean(y) 
  #cat("data:", mean_y, "\n\n")
  
  #model
  model <- lm(y ~ x)
  smmod <- summary(model)
  smmod$information <- paste("data:", mean_y)
  list(summary = smmod, information = smmod$information)
}

[[1]]
[[1]]$summary

Call:
lm(formula = y ~ x)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.36805 -0.22448  0.00118  0.20575  0.45271 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.538509   0.059874   8.994 4.45e-08 ***
x           0.004642   0.058710   0.079    0.938    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2676 on 18 degrees of freedom
Multiple R-squared:  0.0003473, Adjusted R-squared:  -0.05519 
F-statistic: 0.006253 on 1 and 18 DF,  p-value: 0.9378


[[1]]$information
[1] "data: 0.538674127601553"


[[2]]
[[2]]$summary

Call:
lm(formula = y ~ x)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.50855 -0.25834  0.02311  0.28248  0.40919 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.583976   0.071464   8.172 1.81e-07 ***
x           0.003352   0.072773   0.046    0.964    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.3177 on 18 degrees of freedom
Multiple R-squared:  0.0001178, Adjusted R-squared:  -0.05543 
F-statistic: 0.002121 on 1 and 18 DF,  p-value: 0.9638


[[2]]$information
[1] "data: 0.583617433917243"

【讨论】：