R: Problems of wrapping polynomial regression in a function - r

Question

When I was doing polynomial regression, I tried to put fits with different degree of polynomial into a list, so I wrapped the glm into a function:
library(MASS)
myglm <- function(dop) {
# dop: degree of polynomial
glm(nox ~ poly(dis, degree = dop), data = Boston)
}
However, I guess there might be some problem related to lazy evaluation. The degree of the model is parameter dop rather than a specific number.
r$> myglm(2)
Call: glm(formula = nox ~ poly(dis, degree = dop), data = Boston)
Coefficients:
(Intercept) poly(dis, degree = dop)1 poly(dis, degree = dop)2
0.5547 -2.0031 0.8563
Degrees of Freedom: 505 Total (i.e. Null); 503 Residual
Null Deviance: 6.781
Residual Deviance: 2.035 AIC: -1347
When I do cross-validation using this model, an error occurs:
>>> cv.glm(Boston, myglm(2))
Error in poly(dis, degree = dop) : object 'dop' not found
So how can I solve this problem ?

Answer 1

Quosures, quasiquotation, and tidy evaluation are useful here:
library(MASS)
library(boot)
library(rlang)
myglm <- function(dop) {
eval_tidy(quo(glm(nox ~ poly(dis, degree = !! dop), data = Boston)))
}
cv.glm(Boston, myglm(2))