问题
During coding, I need to change the dummy value assigned to a factor. However, the following code does not work. Any suggestion?
test_mx= data.frame(a= c(T,T,T,F,F,F), b= c(1,1,1,0,0,0))
test_mx
a b
1 TRUE 1
2 TRUE 1
3 TRUE 1
4 FALSE 0
5 FALSE 0
6 FALSE 0
model= glm(b ~ a, data= test_mx, family= "binomial")
summary(model)
model= glm(a ~ b, data= test_mx, family= "binomial")
summary(model)
Here I will get the coef for b is 47. Now if I swap the dummy value, it should be -47 then. However, this is not the case.
test_mx2= test_mx
contrasts(test_mx2$a)
TRUE
FALSE 0
TRUE 1
contrasts(test_mx2$a) = c(1,0)
contrasts(test_mx2$a)
[,1]
FALSE 1
TRUE 0
model= glm(a ~ b, data= test_mx2, family= "binomial")
summary(model)
coef for b is still the same. What is going on? Thanks.
回答1:
There are several confusing things regarding your question. You have used both a ~ b and b ~ a, so what are you looking at exactly?
- Contrasts only applies to covariates / independent variables, as it is related to construction of model matrix; So for
a ~ b, contrasts should be applied tob, while forb ~ a, contrasts should be applied toa; - Contrasts only works for factor / logical variables, rather than numerical variables. So unless you have
bas a factor, you can't play contrasts with it.
Without changing data type, it is clear that only a model b ~ a is legitimate for further discussion. In the following, I will show how to set contrasts for a.
Method 1: using contrasts argument of glm and lm
We can control contrasts treatment by the contrasts argument of glm (the same for lm):
## dropping the first factor level (default)
coef(glm(b ~ a, data = test_mx, family = binomial(),
contrasts = list(a = contr.treatment(n = 2, base = 1))))
#(Intercept) a2
# -24.56607 49.13214
## dropping the second factor level
coef(glm(b ~ a, data = test_mx, family = binomial(),
contrasts = list(a = contr.treatment(n = 2, base = 2))))
#(Intercept) a1
# 24.56607 -49.13214
Here, contr.treatment is generating a contrasts matrix:
contr.treatment(n = 2, base = 1)
# 2
#1 0
#2 1
contr.treatment(n = 2, base = 2)
# 1
#1 1
#2 0
and they are passed to glm to effectively change the behaviour of model.matrix.default. Let's compare the model matrix for two cases:
model.matrix.default( ~ a, test_mx, contrasts.arg =
list(a = contr.treatment(n = 2, base = 1)))
# (Intercept) a2
#1 1 1
#2 1 1
#3 1 1
#4 1 0
#5 1 0
#6 1 0
model.matrix.default( ~ a, test_mx, contrasts.arg =
list(a = contr.treatment(n = 2, base = 2)))
# (Intercept) a1
#1 1 0
#2 1 0
#3 1 0
#4 1 1
#5 1 1
#6 1 1
The second column for a is just a flip between 0 and 1, which is what you have expected for a dummy variable.
Method 2: setting "contrasts" attribute to data frame directly
We can use C or contrasts to set "contrasts" attributes (C is only for setting, but contrasts can be used for viewing as well):
test_mx2 <- test_mx
contrasts(test_mx2$a) <- contr.treatment(n = 2, base = 1)
str(test_mx2)
#'data.frame': 6 obs. of 2 variables:
# $ a: Factor w/ 2 levels "FALSE","TRUE": 2 2 2 1 1 1
# ..- attr(*, "contrasts")= num [1:2, 1] 0 1
# .. ..- attr(*, "dimnames")=List of 2
# .. .. ..$ : chr "FALSE" "TRUE"
# .. .. ..$ : chr "2"
# $ b: num 1 1 1 0 0 0
test_mx3 <- test_mx
contrasts(test_mx3$a) <- contr.treatment(n = 2, base = 2)
str(test_mx3)
#'data.frame': 6 obs. of 2 variables:
# $ a: Factor w/ 2 levels "FALSE","TRUE": 2 2 2 1 1 1
# ..- attr(*, "contrasts")= num [1:2, 1] 1 0
# .. ..- attr(*, "dimnames")=List of 2
# .. .. ..$ : chr "FALSE" "TRUE"
# .. .. ..$ : chr "1"
# $ b: num 1 1 1 0 0 0
Now we can fit glm without using contrasts argument:
coef(glm(b ~ a, data = test_mx2, family = "binomial"))
#(Intercept) a2
# -24.56607 49.13214
coef(glm(b ~ a, data = test_mx3, family = "binomial"))
#(Intercept) a1
# 24.56607 -49.13214
Method 3: setting options("contrasts") for a global change
Hahaha, @BenBolker yet mentions another option, which is by setting the global options of R. For your specific example with a factor involving only two levels, we can makes use of ?contr.SAS.
## using R default contrasts options
#$contrasts
# unordered ordered
#"contr.treatment" "contr.poly"
coef(glm(b ~ a, data = test_mx, family = "binomial"))
#(Intercept) aTRUE
# -24.56607 49.13214
options(contrasts = c("contr.SAS", "contr.poly"))
coef(glm(b ~ a, data = test_mx, family = "binomial"))
#(Intercept) aFALSE
# 24.56607 -49.13214
But I believe Ben is just mention this to complete the picture; He will not take this way in reality, as changing global options is not good for getting reproducible R code.
Another issue is that contr.SAS will just treat the last factor level as reference. In your particular case with only 2 levels, this effectively does the "flipping".
Method 4: Manually recoding your factor levels
I had no intention to mention this as it is so trivial, but since I have added "Method 3", I'd better add this one, too.
test_mx4 <- test_mx
test_mx4$a <- factor(test_mx4$a, levels = c("TRUE", "FALSE"))
coef(glm(b ~ a, data = test_mx4, family = "binomial"))
#(Intercept) aTRUE
# -24.56607 49.13214
test_mx5 <- test_mx
test_mx5$a <- factor(test_mx5$a, levels = c("FALSE", "TRUE"))
coef(glm(b ~ a, data = test_mx5, family = "binomial"))
#(Intercept) aFALSE
# 24.56607 -49.13214
回答2:
As pointed out by Zheyuan, Contrasts only control dummy value assignment for categorical predictors (x values) but not for categorical response (y value) in glm modeling. I have reported this issue to R core team.
To assign dummy value manually for predictors, another way can be direct assignment by a vector/matrix such as
contrasts(test_mx$a) = c(1,0)
However, there is a risk to do so: if later in the code you try to use test_mx$a as a response value in modeling, dummy value assignment can be confusing, as the assignment there will NOT match with
contrasts(test_mx$a).
来源：https://stackoverflow.com/questions/39802426/how-to-set-contrasts-for-my-variable-in-regression-analysis-with-r