R: modeling on residuals

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I have heard people talk about "modeling on the residuals" when they want to calculate some effect after an a-priori model has been made. For example, if they know that two variables, var_1 and var_2 are correlated, we first make a model with var_1 and then model the effect of var_2 afterwards. My problem is that I've never seen this done in practice.

I'm interested in the following:

  1. If I'm using a glm, how do I account for the link function used?
  2. What distribution do I choose when running a second glm with var_2 as explanatory variable? I assume this is related to 1.
  3. Is this at all related to using the first models prediction as an offset in the second model?

My attempt:

dt <- data.table(mtcars) # I have a hypothesis that `mpg` is a function of both `cyl` and `wt`
dt[, cyl := as.factor(cyl)]
model <- stats::glm(mpg ~ cyl, family=Gamma(link="log"), data=dt) # I want to model `cyl` first
dt[, pred := stats::predict(model, type="response", newdata=dt)]
dt[, res := mpg - pred]

# will this approach work?
model2_1 <- stats::glm(mpg ~ wt + offset(pred), family=Gamma(link="log"), data=dt)
dt[, pred21 := stats::predict(model2_1, type="response", newdata=dt) ]

# or will this approach work?
model2_2 <- stats::glm(res ~ wt, family=gaussian(), data=dt)
dt[, pred22 := stats::predict(model2_2, type="response", newdata=dt) ]

My first suggested approach has convergence issues, but this is how my silly brain would approach this problem. Thanks for any help!

glm modeling r
1 Answers

In a sense, an ANCOVA is 'modeling on the residuals'. The model for ANCOVA is y_i = grand_mean + treatment_i + b * (covariate - covariate_mean_i) + error for each treatment i. The term (covariate - covariate_mean_i) can be seen as the residuals of a model with covariate as DV and treatment as IV.

The following regression is equivalent to this ANCOVA:

lm(y ~ treatment * scale(covariate, scale = FALSE))

Which applied to the data would look like this:

lm(mpg ~ factor(cyl) * scale(wt, scale = FALSE), data = mtcars)

And can be turned into a glm similar to the one you use in your example:

glm(mpg ~ factor(cyl) * scale(wt, scale = FALSE), 
    family=Gamma(link="log"), 
    data = mtcars)
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