Setting Different Levels of constants for categorical variables in R - r

Question

Will anyone be able to explain how to set constants for different levels of categorical variables in r?
I have read the following: How to set the Coefficient Value in Regression; R and it does a good job for explaining how to set a constant for the whole of a categorical variable. I would like to know how to set one for each level.
As an example, let us look at the MTCARS dataset:
df <- as.data.frame(mtcars)
df$cyl <- as.factor(df$cyl)
set.seed(1)
glm(mpg ~ cyl + hp + gear, data = df)
This gives me the following output:
Call: glm(formula = mpg ~ cyl + hp + gear, data = df)
Coefficients:
(Intercept) cyl6 cyl8 hp gear
19.80268 -4.07000 -2.29798 -0.05541 2.79645
Degrees of Freedom: 31 Total (i.e. Null); 27 Residual
Null Deviance: 1126
Residual Deviance: 219.5 AIC: 164.4
If I wanted to set cyl6 to -.34 and cyl8 to -1.4, and then rerun to see how it effects the other variables, how would I do that?

Answer 1

I think this is what you can do
df$mpgCyl=df$mpg
df$mpgCyl[df$cyl==6]=df$mpgCyl[df$cyl==6]-0.34
df$mpgCyl[df$cyl==8]=df$mpgCyl[df$cyl==8]-1.4
model2=glm(mpgCyl ~ hp + gear, data = df)
> model2
Call: glm(formula = mpgCyl ~ hp + gear, data = df)
Coefficients:
(Intercept) hp gear
16.86483 -0.07146 3.53128
UPDATE withe comments:
cyl is a factor, therefore by default it contributes to glm as offset, not slope. Actually cyl==4 is 'hidden' but existing in the glm as well. So in your first glm what the models says is:
1) for cyl==4: mpg=19.8-0.055*hp+2.79*gear
2) for cyl==6: mpg=(19.8-4.07)-0.055*hp+2.79*gear
3) for cyl==8: mpg=(19.8-2.29)-0.055*hp+2.79*gear
Maybe you can also check here https://stats.stackexchange.com/questions/213710/level-of-factor-taken-as-intercept and here Is there any way to fit a `glm()` so that all levels are included (i.e. no reference level)?
Hope this helps