day 8 discussion

Set Up

rm(list = ls())
cat("\f")

library(help = "datasets")
?USJudgeRatings

starting httpd help server ... done

View(USJudgeRatings)
clean_df <- USJudgeRatings

library(visdat)
vis_dat(USJudgeRatings)

library(stargazer)

Please cite as: 

 Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
 R package version 5.2.3. https://CRAN.R-project.org/package=stargazer 

stargazer(USJudgeRatings, type = "text",
          covariate.labels = c("Contempt","Integrity","Diligence", "Case Flow Management","Decisiveness","Preparation","Familiarity","Sound Oral Rulings","Sound Written Rulings", "Physical Ability","Judicial Appointments", "Worthy of Retention" ))

====================================================
Statistic             N  Mean  St. Dev.  Min   Max  
----------------------------------------------------
Contempt              43 7.437  0.941   5.700 10.600
Integrity             43 8.021  0.770   5.900 9.200 
Diligence             43 7.516  1.144   4.300 9.000 
Case Flow Management  43 7.693  0.901   5.100 9.000 
Decisiveness          43 7.479  0.860   5.400 8.700 
Preparation           43 7.565  0.803   5.700 8.800 
Familiarity           43 7.467  0.953   4.800 9.100 
Sound Oral Rulings    43 7.488  0.949   5.100 9.100 
Sound Written Rulings 43 7.293  1.010   4.700 8.900 
Physical Ability      43 7.384  0.961   4.900 9.000 
Judicial Appointments 43 7.935  0.940   4.700 9.100 
Worthy of Retention   43 7.602  1.101   4.800 9.200 
----------------------------------------------------

#Cleaning Data
clean_df <- USJudgeRatings
clean_df$CONT <- as.numeric(clean_df$CONT)
clean_df$INTG <- NULL                    # irrelevant
clean_df$CFMG <- NULL

str(USJudgeRatings)

'data.frame':   43 obs. of  12 variables:
 $ CONT: num  5.7 6.8 7.2 6.8 7.3 6.2 10.6 7 7.3 8.2 ...
 $ INTG: num  7.9 8.9 8.1 8.8 6.4 8.8 9 5.9 8.9 7.9 ...
 $ DMNR: num  7.7 8.8 7.8 8.5 4.3 8.7 8.9 4.9 8.9 6.7 ...
 $ DILG: num  7.3 8.5 7.8 8.8 6.5 8.5 8.7 5.1 8.7 8.1 ...
 $ CFMG: num  7.1 7.8 7.5 8.3 6 7.9 8.5 5.4 8.6 7.9 ...
 $ DECI: num  7.4 8.1 7.6 8.5 6.2 8 8.5 5.9 8.5 8 ...
 $ PREP: num  7.1 8 7.5 8.7 5.7 8.1 8.5 4.8 8.4 7.9 ...
 $ FAMI: num  7.1 8 7.5 8.7 5.7 8 8.5 5.1 8.4 8.1 ...
 $ ORAL: num  7.1 7.8 7.3 8.4 5.1 8 8.6 4.7 8.4 7.7 ...
 $ WRIT: num  7 7.9 7.4 8.5 5.3 8 8.4 4.9 8.5 7.8 ...
 $ PHYS: num  8.3 8.5 7.9 8.8 5.5 8.6 9.1 6.8 8.8 8.5 ...
 $ RTEN: num  7.8 8.7 7.8 8.7 4.8 8.6 9 5 8.8 7.9 ...

fullreg <-
  lm(formula = CONT ~ . , 
     data = clean_df)
library(MASS)
stepAIC(object = fullreg,
        direction = "backward")

Start:  AIC=-4.35
CONT ~ DMNR + DILG + DECI + PREP + FAMI + ORAL + WRIT + PHYS + 
    RTEN

       Df Sum of Sq    RSS     AIC
- RTEN  1   0.21040 24.622 -5.9760
- DILG  1   0.82876 25.240 -4.9094
- WRIT  1   0.84491 25.256 -4.8819
- PHYS  1   1.04970 25.461 -4.5346
- DECI  1   1.06803 25.479 -4.5037
- FAMI  1   1.13326 25.544 -4.3938
<none>              24.411 -4.3450
- PREP  1   1.20765 25.619 -4.2687
- ORAL  1   2.12354 26.535 -2.7583
- DMNR  1   2.64254 27.054 -1.9253

Step:  AIC=-5.98
CONT ~ DMNR + DILG + DECI + PREP + FAMI + ORAL + WRIT + PHYS

       Df Sum of Sq    RSS     AIC
- DILG  1   0.67502 25.297 -6.8130
- PHYS  1   0.84087 25.462 -6.5320
- WRIT  1   0.87145 25.493 -6.4804
- PREP  1   1.09104 25.712 -6.1116
- FAMI  1   1.12490 25.746 -6.0550
- DECI  1   1.12851 25.750 -6.0490
<none>              24.622 -5.9760
- DMNR  1   2.91906 27.541 -3.1583
- ORAL  1   2.96452 27.586 -3.0874

Step:  AIC=-6.81
CONT ~ DMNR + DECI + PREP + FAMI + ORAL + WRIT + PHYS

       Df Sum of Sq    RSS     AIC
- PREP  1    0.4600 25.756 -8.0381
- PHYS  1    0.6005 25.897 -7.8041
- DECI  1    0.6459 25.942 -7.7289
- FAMI  1    0.8189 26.115 -7.4431
- WRIT  1    0.9652 26.262 -7.2028
<none>              25.297 -6.8130
- DMNR  1    3.1713 28.468 -3.7344
- ORAL  1    3.2495 28.546 -3.6165

Step:  AIC=-8.04
CONT ~ DMNR + DECI + FAMI + ORAL + WRIT + PHYS

       Df Sum of Sq    RSS     AIC
- FAMI  1    0.4084 26.165 -9.3616
- PHYS  1    1.0882 26.845 -8.2586
<none>              25.756 -8.0381
- WRIT  1    1.3754 27.132 -7.8011
- DECI  1    1.7222 27.479 -7.2549
- DMNR  1    2.9352 28.692 -5.3975
- ORAL  1    4.4656 30.222 -3.1629

Step:  AIC=-9.36
CONT ~ DMNR + DECI + ORAL + WRIT + PHYS

       Df Sum of Sq    RSS     AIC
- PHYS  1    1.0027 27.168 -9.7446
<none>              26.165 -9.3616
- DECI  1    2.0388 28.204 -8.1351
- DMNR  1    3.1762 29.341 -6.4351
- ORAL  1    4.1169 30.282 -5.0780
- WRIT  1    5.2394 31.404 -3.5129

Step:  AIC=-9.74
CONT ~ DMNR + DECI + ORAL + WRIT

       Df Sum of Sq    RSS     AIC
<none>              27.168 -9.7446
- DECI  1    1.5072 28.675 -9.4228
- DMNR  1    2.7768 29.944 -7.5600
- ORAL  1    3.2992 30.467 -6.8163
- WRIT  1    4.4821 31.650 -5.1784

Call:
lm(formula = CONT ~ DMNR + DECI + ORAL + WRIT, data = clean_df)

Coefficients:
(Intercept)         DMNR         DECI         ORAL         WRIT  
     7.3180      -0.5912       0.8183       2.8270      -3.0128  

reg1 <-
  lm(formula = CONT ~ DMNR + DECI + ORAL + WRIT, data = clean_df)