Passing a model to emmeans() made from passing a formula to that model generates an error, whereas a model with the formula hand-coded does not.
ProfessorPeregrine opened this issue · 2 comments
When I create a new model (in this case a gls) I'd like to pass in a formula from a previous model (in this case lm). It seems like I could use formula(old_model) and if I do, the gls function works perfectly. But if I pass in that new gls model to emmeans() it fails with this error: Error in call$model[[2]] : object of type 'symbol' is not subsettable. On the other hand, if I create that new gls model by hand-typing the formula, that model works fine with emmeans().
Here is code to replicate the issue:
require(emmeans)
require(nlme)
data<-structure(list(percent = c(2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,
1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2,
2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 1, 1, 1, 1, 1, 1, 1, 1),
cycle = c(4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
density = c(21,
20, 21, 21, 22, 20, 20, 19, 18, 19, 19, 19, 19, 18, 18, 19, 20,
22, 22, 21, 22, 22, 20, 21, 20, 20, 19, 19, 19, 19, 19, 19, 23,
25, 24, 24, 26, 23, 24, 24, 23, 22, 22, 23, 21, 24, 22, 22, 16,
17, 16, 15, 17, 16, 16, 16, 26, 27, 26, 25, 25, 26, 26, 26),
run = c(8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L)), class = "data.frame", row.names = c(NA,
-64L))
formula<-"density~percent*cycle" #selected by user
effect<-"percent*cycle" #selected by user#
data$cycle<-factor(data$cycle)
data$percent<-factor(data$percent)
aov_mod <-lm(formula = formula(formula),data = data)
#later in the app
data$group<-interaction(data[c(1,2)])
gls_formula<-formula(aov_mod)
model<-gls(gls_formula, data = data, weights = varIdent(form = ~1 | group)) #using extracted formula#
model2<-gls(density~percent*cycle, data = data, weights = varIdent(form = ~1 | group)) #using hard coded formula#
emmeans(model,
formula(paste("pairwise ~",gsub(pattern = ":",replacement = "*",x = effect))),
adjust = "tukey",
type = "response",
data=data)
emmeans(model2,
formula(paste("pairwise ~",gsub(pattern = ":",replacement = "*",x = effect))),
adjust = "tukey",
type = "response",
data=data)
Expected behavior
emmeans() should take a valid model regardless of whether that model was created with a hand-typed or extracted formula.
Additional context
I posted this on StackOverflow:https://stackoverflow.com/questions/78664364/why-do-i-need-to-hardcode-the-formula-in-emmeans-instead-of-dynamically-extrac
The responder mentioned this code chunk for a clue on what might be happening:
Lines 343 to 348 in 48ab800
Thank you very much for a great package! I have the workaround, but I hope this helps make the package better!
The problem is not there; for one thing, data is not NULL; and you can check it:
> all.vars(eval(model$call[[2]]))
[1] "density" "percent" "cycle"
But there is a similar place --
Line 419 in 48ab800
... where an extra eval is needed. With that change, which I will push up soon, it works correctly.
Thanks for reporting this.
Closing as I think this is resolved. BTW, the bug is actually in the Satterthwaite code, so if you had used mode = "asymp" or mode = "appx-satt", you wouldn't see this bug.