categorical datacontinuous datageneralized linear model
Age is a predictor variable among a few others (gender, weight and height) and my response is the log-odds of a certain disease (binomial glm).
My age data runs from 21 until 40. I am not sure whether to treat age as a continuous variable or as a factor with age groups: 21-25, 26-30, 31-35, 36-40.
Are there any plots which I can produce that can help determine which would be the better approach?
Here's what's happening:
For binomial and quasibinomial families the response can also be specified as a factor (when the first level denotes failure and all others success) or as a two-column matrix with the columns giving the numbers of successes and failures.
Taken from the glm.fit docs.
A few points:
lm) vs generalized linear (glm) models depends on the response variable (Height_cm), not on anything having to do with the predictor variables. Since your response is continuous, you definitely should avoid the standard GLMs (Poisson, binomial/logistic) which are meant for count or proportion data.plot(Model4, col = as.numeric(Data.dat.complete$Gender)) to check this.You may find the annotated version of the diagnostics from the performance::diagnostics() function useful (I don't agree with all of the design decisions, e.g. the fifth plot showing "normality of residuals" is redundant and less revealing than the Q-Q plot, but overall it's helpful).
Best Answer
It depends on the context. For example if you are looking for the effect of age on children's height, it makes sense to look at it as a continuous ( integer) value. If you're looking for e.g. the effect of age on oncogenesis then it makes sense if you look at age groups. Young vs old, above 55 and below 55, ...
For your example, unless age is a confounder of a hidden factor such as for example being college grad or still a student ( risk factor for young adults STD infection), I'd bin my data into reasonable bin sizes.