降低水平以将其中 2 个视为控制案例。 回归/建模/统计问题，因为它不是虚拟的？

Question

I've stumbled upon a doubt about using droplevels in my dataset. I have 4 factors in my "Disease column".

BD$Etiología <- factor(BD$Etiología, levels=c(0,1,2,3,4) ,
labels= c("Control","Idiop","LMNA","BAG3","Isquémica"), ordered=FALSE)

Then i make a subset in order to just compare the Control Cases vs 1 of the diseases.

BD_C_ID <- subset(BD, Etiología=="Control" | Etiología=="Idiop")

BD_C_ID$Etiología= droplevels(BD_C_ID$Etiología) 

BD_C_ID$Etiología

[1] Control Control Control Control Control Control Control Idiop   Idiop   Control Control Control
[13] Control Idiop   Idiop   Idiop   Idiop   Idiop   Idiop   Idiop   Idiop   Idiop   Idiop   Idiop  
[25] Idiop   Idiop   Control Control Control Control Idiop   Control Control Control Control Control
[37] Idiop   Idiop   Idiop   Idiop  
Levels: Control Idiop

Since the first factor was unordered, and i just drop the levels i don't use. Could i treat them as a 0-1 coded value in order to use them in a lm , or a logistic regression? Or will there be a problem?

Also, does that apply if i use the Control VS BAG3 (0-3 in the initial code?)? Or will i need to re-level them so its 0-1 re-applying factors?

Answer 1

Short answer is it doesn't matter. If you use them in a linear model lm or logistic regression, the model will use the first level as a reference level, so in this case, it is always "Control" . The droplevels() is good if you need to perform some functions with the factors, but if it is purely for lm() or glm() , these functions takes care of the factors underneath.

To illustrate this using your example:

set.seed(111)
BD = data.frame(
          Etiologia = sample(0:4,100,replace=TRUE),
          x = rnorm(100),
          y = rnorm(100)
                )

We can just do:

BD$E <- factor(BD$Etiologia,levels=0:4,
labels= c("Control","Idiop","LMNA","BAG3","Isquemica"))

lm(y ~ x + E,data=subset(BD,E %in% c("Control","Idiop")))

Call:
lm(formula = y ~ x + E, data = subset(BD, E %in% c("Control", "Idiop")))

Coefficients:
(Intercept)            x       EIdiop  
   -0.05524      0.21596      0.30433 

And using another comparison:

lm(y ~ x + E,data=subset(BD,E %in% c("Control","BAG3")))

     Call:
lm(formula = y ~ x + E, data = subset(BD, E %in% c("Control", 
    "BAG3")))

Coefficients:
(Intercept)            x        EBAG3  
   -0.03355      0.08978     -0.21708  

You get the same result if you do:

BD$Etiologia <- factor(BD$Etiologia, levels=c(0,1,2,3,4) ,
labels= c("Control","Idiop","LMNA","BAG3","Isquemica"), ordered=FALSE)

BD_C_ID <- droplevels(subset(BD, Etiologia=="Control" | Etiologia=="Idiop"))

lm(y ~ x + Etiologia,data=BD_C_ID)

Call:
lm(formula = y ~ x + Etiologia, data = BD_C_ID)

Coefficients:
   (Intercept)               x  EtiologiaIdiop  
      -0.05524         0.21596         0.30433