Create a nice table from one or more regression models

Create a nice table from one or more regression models

nice_regression_table(
  ...,
  round = 2,
  labels_models = NULL,
  rename_labels = list(),
  rename_cols = list(),
  remove_cols = NULL,
  auto_col_names = TRUE,
  file = NULL,
  or = FALSE,
  nice_p = TRUE,
  title = "Regression model",
  footnote = NULL
)

Arguments

remove_cols

Either column number or column names to be removed

or

If TRUE, the estimators are assumed to be logits and are exponentiated to yield odds ratios

Examples

lm(mpg ~ am + disp + hp, data = mtcars) |> 
  nice_regression_table()



  
Table
Regression model		
    
Predictor
      
        
mpg

      		
    
Estimate
      SE
      t
      p
    

    
(Intercept)
27.87
1.62
17.2
<.001***
am
3.8
1.42
2.67
.013*
disp
-0.01
0.01
-1.55
.133
hp
-0.04
0.01
-3.03
.005**
Model
    
R²
0.8





R² adjusted
0.78





Observations
32





  
nice_regression_table(
  nlme::lme(mpg~disp, data = mtcars, random = ~1|am),
  nlme::lme(mpg~disp + hp, data = mtcars, random = ~1|am)
)   



  
Table
Regression model		
    
Predictor
      
        
mpg

      		
      
        
mpg 1

      		
    
Estimate
      SE
      DF
      t
      p-value
      Estimate
      SE
      DF
      t
      p-value
    

    
(Intercept)
29.28
1.4
29
20.85
<.001***
29.9
2.16
28
13.83
<.001***
disp
-0.04
0.01
29
-7.71
<.001***
-0.02
0.01
28
-1.86
.073
hp










-0.04
0.01
28
-2.9
.007**
Model
    
(Intercept) am
0.65








6.19







Residual
10.35








8.08







ICC
0.06








0.43







N am
2








2







R² Conditional
0.7








0.76







R² Marginal
0.69








0.58







Observations
32








32







  
nice_regression_table(
  wmisc:::model_lmer_1, wmisc:::model_lmer_2,
  rename_labels = list(
    "EffectTrend" = "Trend", "EffectSlope" = "Slope", "TimePost" = "Post", 
    "ConditionTraining" = "Training", "id_subject" = "Subject"),
  rename_cols = list("Estimate" = "B", "SE" = "se"),
  labels_models = c("Only pretest", "Pre- and posttest") 
)
#> Loading required namespace: lmerTest



  
Table
Regression model		
    
Predictor
      
        
Only pretest

      		
      
        
Pre- and posttest

      		
    
B
      se
      df
      t
      p
      B
      se
      df
      t
      p
    

    
(Intercept)
-0.07
0.06
192.42
-1.1
.273
-0.05
0.09
224.18
-0.51
.610
Trend
-0.11
0.06
300
-2.03
.043*
-0.2
0.08
484.83
-2.6
.010**
Slope
-0.33
0.06
300
-5.81
<.001***
-0.43
0.08
484.83
-5.6
<.001***
Trend & Slope
0.76
0.06
300
13.47
<.001***
0.78
0.08
484.83
10.17
<.001***
Post










-0.15
0.07
240.04
-2.02
.044*
Training










-0.04
0.12
224.18
-0.31
.758
Post:Training










0.16
0.1
240.04
1.69
.091
Post:Trend










0.24
0.07
7569.02
3.29
.001**
Post:Slope










0.16
0.07
7569.02
2.28
.023*
Post:Trend & Slope










0.07
0.07
7569.02
0.97
.332
Training:Trend










0.15
0.1
484.83
1.46
.144
Training:Slope










0.18
0.1
484.83
1.74
.083
Training:Trend & Slope










-0.03
0.1
484.83
-0.33
.740
Post:Training:Trend










-0.26
0.09
7569.02
-2.71
.007**
Post:Training:Slope










0.01
0.09
7569.02
0.07
.940
Post:Training:Trend & Slope










-0.33
0.09
7569.02
-3.51
<.001***
Model
    
(Intercept) Subject
0.24








0.19







(Intercept) Subject:Effect
0.11








0.07







(Intercept) Subject:Time










0.06







Residual
0.55








0.55







ICC
0.39








0.37







N Subject
101








101







N Effect
4








4







N Time










2







R² Conditional
0.48








0.45







R² Marginal
0.16








0.14







Observations
4040








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