This came up recently in a discussion of lm() on the R-devel list. I'd
assume the same issue applies to glm.
The problem is that the argument to weights is evaluated in the same way
as arguments in the formula: first in data, then in the environment of
the formula. The latter will eventually lead back to the global
environment, but won't lead to the local evaluation frame in myglm().
The easiest solution is to add newweights to the data argument, but
there are a few gotchas here.
First, if newweights is already a column in data, you'll mess things up.
So be sure to use a name that can't be there. That's okay in your
example.
The second problem is that a dot in the formula will cause problems,
because it will try to include newweights as a predictor variable. It's
possible to work around this, but it's probably better to use a more
complicated solution instead: modify the formula environment so it
starts with a small environment holding newweights. You don't want to
add newweights directly to environment(formula), because that will have
side effects outside your function.
This version of your function takes this more complicated approach:
myglm <- function(formula, data, weights){
## this works
print(glm(formula, data, family=gaussian(), weights))
env <- new.env(parent = environment(formula))
env$newweights <- rep(1, n)
environment(formula) <- env
glm(formula, data, family=gaussian(), weights=newweights)
}
Duncan Murdoch
On 28/08/2020 11:32 a.m., John Smith wrote:
Dear R-help:
I am writing a function based on glm and would like some variations of
weights. In the code below, I couldn't understand why the second glm
function fails and don't know how to fix it:
Error in eval(extras, data, env) : object 'newweights' not found
Calls: print ... eval -> <Anonymous> -> model.frame.default -> eval -> eval
Execution halted
### R code
y <- rnorm(100)
x <- rnorm(100)
data <- data.frame(cbind(x, y))
weights <- rep(1, 100)
n <- 100
myglm <- function(formula, data, weights){
## this works
print(glm(formula, data, family=gaussian(), weights))
## this is not working
newweights <- rep(1, n)
glm(formula, data, family=gaussian(), weights=newweights)
}
myglm(y~., data, weights)
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