Updating model factor levels within function not working properly - r

Question

I'm running some GLM models in R on a some data related to feeding trials I am doing. I'm regressing my variables of interest on a two predictors: one factor with three levels and one continuous variable. I want to compare the intercepts for each level of the factor to one another to determine if they're different. To do this, I wrote a function (called interceptCompare in the reproducible code below) which relevels the factor and updates the model and then saves the results of each model. It's my quick way of doing all the pair-wise comparisons of the intercepts.
The problem is that when I run the function, it doesn't appear to properly update the model. Each item of the list returned is the same, when they should be changing so that each item has a different level of the factor as the "(Intercept)" that the other levels are being compared against. I suspect it has something to do with the environment of the function, but I'm not sure. I haven't been able to find a similar example on stackoverflow or google.
Here's what should be a reproducible example:
food <- as.factor(rep(c("a", "b", "c"), each = 20))
variable <- rbinom(60, 1, 0.7)
movement <- rgamma(60, 10, 2)
binomial.model <- glm(variable ~ food,
family = "binomial")
gamma.model <- glm(movement ~ food,
family = Gamma)
interceptCompare <- function(model, factor) {
results <- list() # empty list to store results
for (i in unique(factor)) {
factor <- relevel(factor, ref = i)
model <- update(model)
results[[i]] <- summary(model)$coefficients[1:3, ]
}
results <- lapply(results, function(x) round(x, 4))
return(results)
}
interceptCompare(binomial.model, food)
interceptCompare(gamma.model, food)

Answer 1

You will need to add one line, in order to change the data, and use it within the update:
interceptCompare <- function(model, factor) {
results <- list() # empty list to store results
s <- deparse(substitute(factor))#ADD THIS LINE
for (i in unique(factor)) {
factor <- relevel(factor, ref = i)
model[["model"]][[s]] <- factor #CHANGE THE DATA IN THE MODEL
model <- update(model,data=model[["model"]])# UPDATE THE MODEL
results[[i]] <- summary(model)$coefficients[1:3, ]
}
results <- lapply(results, function(x) round(x, 4))
return(results)
}
interceptCompare(binomial.model, food)
$a
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.3863 0.5590 2.4799 0.0131
foodb -0.7673 0.7296 -1.0516 0.2930
foodc -0.2877 0.7610 -0.3780 0.7054
$b
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.6190 0.4688 1.3205 0.1867
fooda 0.7673 0.7296 1.0516 0.2930
foodc 0.4796 0.6975 0.6876 0.4917
$c
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.0986 0.5164 2.1275 0.0334
foodb -0.4796 0.6975 -0.6876 0.4917
fooda 0.2877 0.7610 0.3780 0.7054
interceptCompare(gamma.model, food)
$a
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2246 0.0156 14.3919 0.0000
foodb -0.0170 0.0213 -0.8022 0.4257
foodc -0.0057 0.0218 -0.2608 0.7952
$b
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2076 0.0144 14.3919 0.0000
fooda 0.0170 0.0213 0.8022 0.4257
foodc 0.0114 0.0210 0.5421 0.5898
$c
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2189 0.0152 14.3919 0.0000
foodb -0.0114 0.0210 -0.5421 0.5898
fooda 0.0057 0.0218 0.2608 0.7952

Answer 2

You need to be much more careful when trying to swap out symbols from formulas which is what you are doing. You need to put it in terms that the R language can understand. You want to pass in the name "food" not the values stored in the "food" vector as you are now. Here's an update that seems to do what you were trying to do
interceptCompare <- function(model, factor) {
sym <- substitute(factor)
results <- list() # empty list to store results
for (i in unique(factor)) {
change <- eval(bquote(~.-.(sym)+relevel(.(sym), ref=.(i))))
new_model <- update(model, change)
results[[i]] <- summary(new_model)$coefficients[1:3, ]
}
results <- lapply(results, function(x) round(x, 4))
return(results)
}
Here we capture the name "food" with the substitute. Then we use bquote() to build a new formula that will remove the original value, and relevel the factor variable with a particular reference. Then we save this to a new object so we don't keep updating the same model. For the binomial.model, this returns
$`a`
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.8473 0.4879 1.7364 0.0825
relevel(food, ref = "a")b 0.0000 0.6901 0.0000 1.0000
relevel(food, ref = "a")c -0.8473 0.6619 -1.2801 0.2005
$b
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.8473 0.4879 1.7364 0.0825
relevel(food, ref = "b")a 0.0000 0.6901 0.0000 1.0000
relevel(food, ref = "b")c -0.8473 0.6619 -1.2801 0.2005
$c
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.0000 0.4472 0.0000 1.0000
relevel(food, ref = "c")a 0.8473 0.6619 1.2801 0.2005
relevel(food, ref = "c")b 0.8473 0.6619 1.2801 0.2005
You can see how it changed the ref= at each iteration