“找不到对象'mtcars1'”：olsrr 函数中的错误

Question

当我尝试使用 olsrr 中的函数（如 ols_step_both_p()）时，我收到一个找不到对象的错误，如下所示：

eval 中的错误（model$call$data）：找不到对象“mtcars1”

我想可能是我使用的数据框搞砸了，因为有一列带有日期，另一列带有整数。所以我尝试使用'mtcars'上的功能。

有效！太棒了....但是当我对 mtcars 进行小幅更改时....比如创建一个名为 mtcars1 的数据帧的副本，我又遇到了错误。

test_step_regress <- function()
{
   mtcars1 <- mtcars
   model <- lm(mpg ~ ., data = mtcars1)
   k1 <- ols_step_both_p(model)
   print(k1)
}

我得到的错误：

Error in eval(model$call$data) : object 'mtcars1' not found 
5.
eval(model$call$data) 
4.
eval(model$call$data) 
3.
ols_step_both_p.default(model) 
2.
ols_step_both_p(model) at regress_step.r#5
1.
test_step_regress()

我的会话信息（）：

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Linux Mint 18.3

Matrix products: default
BLAS:   /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

Random number generation:
 RNG:     Mersenne-Twister 
 Normal:  Inversion 
 Sample:  Rounding 

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods  
[7] base

Answer 1

在model 中重新分配call 属性可以解决问题

test_step_regress <- function(){ 

 mtcars1 <- mtcars
 model <- lm(mpg ~ ., data = mtcars1)
 print("model call data after creating the model")
 print(model$call$data)
 model$call$data <- mtcars1
 print("model call data after resassigning")
 print(model$call$data)

   k1 <- ols_step_both_p(model)
   print(k1)     

}

test_step_regress()
#[1] "model call data after creating the model"
#mtcars1
#[1] "model call data after resassigning"
#                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#...
#..
#Stepwise Selection Method   
#---------------------------

#Candidate Terms: 

#1. cyl 
#2. disp 
#3. hp 
#4. drat 
#5. wt 
#6. qsec 
#7. vs 
#8. am 
#9. gear 
#10. carb 

#We are selecting variables based on p value...

#Variables Entered/Removed: 

#✔ wt 
#✔ cyl 

#No more variables to be added/removed.


#Final Model Output 
#------------------

#                        Model Summary                          
#--------------------------------------------------------------
#R                       0.911       RMSE                2.568 
#R-Squared               0.830       Coef. Var          12.780 
#Adj. R-Squared          0.819       MSE                 6.592 
#Pred R-Squared          0.790       MAE                 1.921 
#--------------------------------------------------------------
# RMSE: Root Mean Square Error 
# MSE: Mean Square Error 
# MAE: Mean Absolute Error 

#                               ANOVA                                 
#--------------------------------------------------------------------
#                Sum of                                              
#               Squares        DF    Mean Square      F         Sig. 
#--------------------------------------------------------------------
#Regression     934.875         2        467.438    70.908    0.0000 
#Residual       191.172        29          6.592                     
#Total         1126.047        31                                    
#--------------------------------------------------------------------

                                  Parameter Estimates                                    
#----------------------------------------------------------------------------------------
#      model      Beta    Std. Error    Std. Beta      t        Sig      lower     upper 
#----------------------------------------------------------------------------------------
#(Intercept)    39.686         1.715                 23.141    0.000    36.179    43.194 
#         wt    -3.191         0.757       -0.518    -4.216    0.000    -4.739    -1.643 
#        cyl    -1.508         0.415       -0.447    -3.636    0.001    -2.356    -0.660 
#----------------------------------------------------------------------------------------

                             Stepwise Selection Summary                               
#-------------------------------------------------------------------------------------
                     Added/                   Adj.                                       
#Step    Variable    Removed     R-Square    R-Square     C(p)        AIC        RMSE     
#-------------------------------------------------------------------------------------
#   1       wt       addition       0.753       0.745    11.6270    166.0294    3.0459    
#   2      cyl       addition       0.830       0.819     1.2190    156.0101    2.5675    
#-------------------------------------------------------------------------------------