Error in Logistic Regression for Factors in R - r

Question

I am trying to do logistic regression by using the code:
model <- glm (Participation ~ Gender + Race + Ethnicity + Education + Comorbidities + WLProgram + LoseWeight + EverLoseWeight + PastYearLW + Age + BMI, data = LogisticData, family = binomial)
summary(model)
I keep getting the error:
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels
Upon checking the forums I checked to see which variables were factors:
str(LogisticData)
'data.frame': 994 obs. of 13 variables:
$ outcome : Factor w/ 2 levels "No","Yes": 1 1 2 2 1 2 2 1 2 2 ...
$ Gender : Factor w/ 3 levels "Male","Female",..: 1 2 2 1 2 1 1 1 1
$ Race : Factor w/ 3 levels "White","Black",..: 1 1 1 3 1 1 1 1 1 1
$ Ethnicity : Factor w/ 2 levels "Hispanic/Latino",..: 2 2 2 2 2 2 2 2 2
$ Education : Factor w/ 2 levels "Below Bachelors",..: 1 1 1 2 1 1 1 2 1
$ Comorbidities : Factor w/ 2 levels "No","Yes": 1 1 2 1 1 1 2 2 1 1 ...
$ WLProgram : Factor w/ 2 levels "No","Yes": NA 1 2 2 1 1 1 NA 1 1 ...
$ LoseWeight : Factor w/ 2 levels "Yes","No": 2 1 1 1 1 1 1 2 1 1 ...
$ PastYearLW : Factor w/ 2 levels "Yes","No": NA 2 1 1 1 2 1 NA 1 1 ...
$ EverLoseWeight: Factor w/ 2 levels "Yes","No": 2 1 1 1 1 1 1 2 1 1 ...
$ Age : int 29 35 69 32 21 45 40 62 59 58 ...
$ Participation : Factor w/ 2 levels "Yes","No": 2 2 1 1 1 1 1 2 1 2 ...
$ BMI : num 25.7 33.8 26.4 32.3 27.5 ...
All factors appear to have 2 or more levels.
I also tried to omit NA's which still gave me this error.
I want all variables in the regression, and can't figure out why it won't run.
When performing :
newdata <- droplevels(na.omit(LogisticData))
> str(newdata)
'data.frame': 840 obs. of 13 variables:
$ outcome : Factor w/ 2 levels "No","Yes": 1 2 2 1 2 2 2 2 2 2 ...
$ Gender : Factor w/ 3 levels "Male","Female",..: 2 2 1 2 1 1 1 2 1
$ Race : Factor w/ 3 levels "White","Black",..: 1 1 3 1 1 1 1 1 3
$ Ethnicity : Factor w/ 2 levels "Hispanic/Latino",..: 2 2 2 2 2 2 2 2
$ Education : Factor w/ 2 levels "Below Bachelors",..: 1 1 2 1 1 1 1 1
$ Comorbidities : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 2 1 1 1 2 ...
$ WLProgram : Factor w/ 2 levels "No","Yes": 1 2 2 1 1 1 1 1 1 1 ...
$ LoseWeight : Factor w/ 1 level "Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ PastYearLW : Factor w/ 2 levels "Yes","No": 2 1 1 1 2 1 1 1 1 2 ...
$ EverLoseWeight: Factor w/ 1 level "Yes": 1 1 1 1 1 1 1 1 1 1 ...
$ Age : int 35 69 32 21 45 40 59 58 23 32 ...
$ Participation : Factor w/ 2 levels "Yes","No": 2 1 1 1 1 1 1 2 2 1 ...
$ BMI : num 33.8 26.4 32.3 27.5 45.4 ...
- attr(*, "na.action")=Class 'omit' Named int [1:154] 1 8 13 14 21 24 25
46 55 58 ...
.. ..- attr(*, "names")= chr [1:154] "1" "8" "13" "14" ...
This doesn't make sense to me because you can see in the first str(Logisitic Data) that there is obviously 2 levels in EverLoseWeight as you can see both the Yes and the No and the 1 and 2? How do I fix this anomaly?

Answer 1

Given your update, it looks like you have at least two possibilities.
1: Remove the factors that are left with only a single level after removing the NAs (i.e. LoseWeight and EverLoseWeight).
2: Treat the NAs as an extra level. Something along the lines of
a = as.factor(c(1,1,NA,2))
b = as.factor(c(1,1,2,1))
# 0 is an unused factor level for a
x = data.frame(a, b)
levels(x$a) = c(levels(x$a), 0)
x$a[is.na(x$a)] = 0
But this might not deal with any singularity issues that also resulted in having single-level factors.

Answer 2

Try doing summary on your raw data and make sure that all of the levels have values. I would put this in a comment, but I don't have the reputation points :(