categorical variable in logistic regression in r - r

Question

how I have to implement a categorical variable in a binary logistic regression in R? I want to test the influence of the professional fields (student, worker, teacher, self-employed) on the probability of a purchase of a product.
In my example y is a binary variable (1 for buying a product, 0 for not buying). - x1: is the gender (0 male, 1 female)
- x2: is the age (between 20 and 80)
- x3: is the categorical variable (1=student, 2=worker, 3=teacher, 4=self-employed)
set.seed(123)
y<-round(runif(100,0,1))
x1<-round(runif(100,0,1))
x2<-round(runif(100,20,80))
x3<-round(runif(100,1,4))
test<-glm(y~x1+x2+x3, family=binomial(link="logit"))
summary(test)
If I implement x3 (the professional fields) in my regression above, I get the wrong estimates/interpretation for x3.
What I have to do to get the right influence/estimates for the categorical variable (x3)?
Thanks a lot

Answer 1

I suggest you to set x3 as a factor variable, there is no need to create dummies:
set.seed(123)
y <- round(runif(100,0,1))
x1 <- round(runif(100,0,1))
x2 <- round(runif(100,20,80))
x3 <- factor(round(runif(100,1,4)),labels=c("student", "worker", "teacher", "self-employed"))
test <- glm(y~x1+x2+x3, family=binomial(link="logit"))
summary(test)
Here is the summary:
This is the output of your model:
Call:
glm(formula = y ~ x1 + x2 + x3, family = binomial(link = "logit"))
Deviance Residuals:
Min 1Q Median 3Q Max
-1.4665 -1.1054 -0.9639 1.1979 1.4044
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.464751 0.806463 0.576 0.564
x1 0.298692 0.413875 0.722 0.470
x2 -0.002454 0.011875 -0.207 0.836
x3worker -0.807325 0.626663 -1.288 0.198
x3teacher -0.567798 0.615866 -0.922 0.357
x3self-employed -0.715193 0.756699 -0.945 0.345
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 138.47 on 99 degrees of freedom
Residual deviance: 135.98 on 94 degrees of freedom
AIC: 147.98
Number of Fisher Scoring iterations: 4
In any case, I suggest you to study this post on R-bloggers:
https://www.r-bloggers.com/logistic-regression-and-categorical-covariates/