控制r中pmap输出的顺序

Question

我有很多统计模型要运行，我正在尝试使用pmap来遍历每个模型所需的变量名。我想返回模型摘要以及关于每个模型的额外信息。我可以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，输出首先返回所有额外信息，然后返回所有模型摘要。每次迭代是否可能同时返回额外的信息和模型摘要?下面是一个简单的例子。

#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, "nn")

#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，我的输出看起来像这样:

>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

Answer 1

让输出在一个块中

regressor <- function(y, x){
#extra information
mean_y <- mean(y) 
#cat("data:", mean_y, "nn")

#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"