如何省略冗余的庞大信息？

Question

我想创建一个将在面板回归中执行的函数：

我目前的工作

library(plm)
fit_panel_lr <- function(y, x) {

  x        <- cbind(as.data.frame(x),as.data.frame(y))
  varnames <- names(x)[3:(length(x))]
  varnames <- varnames[!(varnames == names(y))]
  form     <- paste0(varnames, collapse = "+")
  x_copy   <- data.frame(x)
  form     <- as.formula(paste0(names(y), "~", form))

  params   <- list(
    formula = form, data = x_copy, model = 'within'
  )
  pglm_env <- list2env(params, envir = new.env())

  model_plm <- do.call("plm", params, envir = pglm_env)


  summary(model_plm)
}

现在让我们看看它是如何工作的：

data("EmplUK", package="plm")
dep_var <- EmplUK['capital']
df1     <- EmplUK[-6]
> fit_panel_lr(dep_var, df1)
Oneway (individual) effect Within Model

Call:
plm(formula = capital ~ sector + emp + wage + output, data = list(
    firm = c(1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 
    4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 
    6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 
    9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 
    11, 11, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 
    13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 
    16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 1 (...)
    )), model = "within")


Unbalanced Panel: n = 123, T = 2-8, N = 866

Residuals:
      Min.    1st Qu.     Median    3rd Qu.       Max. 
-6.7614519 -0.0712417  0.0052943  0.0715363  8.9980402 

Coefficients:
          Estimate  Std. Error t-value Pr(>|t|)    
sector  3.9155e-05  1.1484e-04  0.3409  0.73324    
emp     2.2427e-01  1.0923e-02 20.5328  < 2e-16 ***
wage   -1.9868e-03  1.1987e-02 -0.1657  0.86840    
output -6.0120e-03  2.9003e-03 -2.0729  0.03853 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Total Sum of Squares:    615.32
Residual Sum of Squares: 384.35
R-Squared:      0.37537
Adj. R-Squared: 0.26886
F-statistic: 111.023 on 4 and 739 DF, p-value: < 2.22e-16

如您所见，我在最后得到了摘要，但也得到了很多不必要的数字。有什么办法可以省略这些吗？

我所说的冗余信息是指 firm = c(1,1,...) 之后的这些数字。问题可以这样改写：是否有可能如何从

中删除 data=list(...)

Call:
plm(formula = capital ~ sector + emp + wage + output, data = list

Answer 1

您需要 quote 您的数据框名称并将其包含在传递的参数中，而不需要在参数中包含实际的数据框。但是，您还需要确保您的实际数据框在 pglm_env 中可用，以便在评估引用名称时使用。

fit_panel_lr <- function(y, x) {

  x        <- cbind(as.data.frame(x),as.data.frame(y))
  varnames <- names(x)[3:(length(x))]
  varnames <- varnames[!(varnames == names(y))]
  form     <- paste0(varnames, collapse = "+")
  x_copy   <- data.frame(x)
  form     <- as.formula(paste0(names(y), "~", form))

  params   <- list(
    formula = form, model = 'within', data = quote(data_source)
  )
  
  pglm_env <- list2env(params, envir = new.env())
  pglm_env$data_source <- x_copy

  model_plm <- do.call("plm", params, envir = pglm_env)

  summary(model_plm)
}

现在你可以做：

data("EmplUK", package="plm")
dep_var <- EmplUK['capital']
df1     <- EmplUK[-6]
fit_panel_lr(dep_var, df1)

#> Oneway (individual) effect Within Model
#> 
#> Call:
#> plm(formula = capital ~ sector + emp + wage + output, data = data_source, 
#>     model = "within")
#> 
#> Unbalanced Panel: n = 140, T = 7-9, N = 1031
#> 
#> Residuals:
#>        Min.     1st Qu.      Median     3rd Qu.        Max. 
#> -15.4974740  -0.0708288   0.0034195   0.0744795   9.1816716 
#> 
#> Coefficients:
#>           Estimate  Std. Error t-value Pr(>|t|)    
#> emp     0.18715725  0.01552895 12.0522  < 2e-16 ***
#> wage    0.03372382  0.01610535  2.0940  0.03655 *  
#> output -0.00044427  0.00385459 -0.1153  0.90827    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Total Sum of Squares:    1152.7
#> Residual Sum of Squares: 979.98
#> R-Squared:      0.14986
#> Adj. R-Squared: 0.013909
#> F-statistic: 52.1761 on 3 and 888 DF, p-value: < 2.22e-16

如何省略冗余的庞大信息？

How to omit redundant huge information?

regression

r

function

summary

plm