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library(readr)
library(lsmeans)

my.data <- read_csv("https://raw.githubusercontent.com/deargle/deargle.github.io/master/assets/data/LendingClub_2007_2014_Cleaned_Reduced_NoTargetLeak.csv")

Reference level with an intercept

If we fit a logistic regression model, by default, an intercept will be estimated. In R, by default, the first level is used as the reference level.

m <- glm(loan_status ~ grade, family='binomial', data=my.data)
summary(m)
## 
## Call:
## glm(formula = loan_status ~ grade, family = "binomial", data = my.data)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.5829  -0.6222  -0.4769  -0.3523   2.3710  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -2.74868    0.09153 -30.030  < 2e-16 ***
## gradeB       0.63193    0.10614   5.954 2.62e-09 ***
## gradeC       1.20502    0.10544  11.429  < 2e-16 ***
## gradeD       1.53512    0.11264  13.629  < 2e-16 ***
## gradeE       1.83239    0.14778  12.399  < 2e-16 ***
## gradeF       1.95645    0.26251   7.453 9.14e-14 ***
## gradeG       3.66497    0.84165   4.355 1.33e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 8070.2  on 9999  degrees of freedom
## Residual deviance: 7719.6  on 9993  degrees of freedom
## AIC: 7733.6
## 
## Number of Fisher Scoring iterations: 5

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