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

...

One or more model objects (e.g., objects of class lm, glm, lme, lmerModLmerTest, glmerMod).

round

Number of decimal places to round numeric values.

labels_models

Character vector with labels for each model. If NULL, the model formula is used.

rename_labels

A named list to rename predictor labels.

rename_cols

A named list to rename column names.

remove_cols

Either column number or column names to be removed

auto_col_names

If TRUE, common column names are automatically renamed (e.g., "Std. Error" to "SE", "Pr (>|t|)" to "p", etc.).

file

If provided, the resulting table is also written to the specified file (e.g., an Excel file).

or

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

nice_p

If TRUE, p values are formatted nicely

title

Title of the table.

footnote

Footnote of the table.

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








8080