R - passing object to function based on the NAME of the object

Question

Suppose in R I have multiple GLM objects from multiple glm() function calls.

glm_01
glm_02
...
glm_nn

...and suppose that I want to do all possible pairwise comparisons using a chi-squared or F ANOVA test.

anova(glm_01, glm_02, test = "F")
anova(glm_01, glm_03, test = "F")
anova(glm_01, glm_04, test = "F")
...

I don't want to do this manually because the list of models is quite long. Instead I'd like to grab a list of relevant model objects (anything starting with "glm_") and do all pairwise comparisons automatically. However I'm unsure how to pass the model objects (rather than their names in string form) to the anova() function.

As a simple example:

data(mtcars)

# create some models

glm_01 <- glm(mpg ~ cyl                 , mtcars, family = gaussian())
glm_02 <- glm(mpg ~ cyl + disp          , mtcars, family = gaussian())
glm_03 <- glm(mpg ~ cyl + disp + hp     , mtcars, family = gaussian())
glm_04 <- glm(mpg ~ cyl + disp + hp + wt, mtcars, family = gaussian())

# get list of relevant model objects from the R environment

model_list <- ls()
model_list <- model_list[substr(model_list, 1, 4) == "glm_"]

# create a table to store the pairwise ANOVA results

n_models <- length(model_list)
anova_table <- matrix(0, nrow = n_models, ncol = n_models)

# loop through twice and do pairwise comparisons

for(row_index in 1:n_models) {
  for(col_index in 1:n_models) {
      anova_table[row_index, col_index] <- anova(model_list[row_index], model_list[col_index], test = "F")$'Pr(>F)'[2]
  }
}

...but of course this loop at the end doesn't work because I'm not passing model objects to anova(), I'm passing the names of the objects as strings instead. How do I tell anova() to use the object that the string refers to, instead of the string itself?

Thank you.

======================

Possible solution:

data(mtcars)

glm_list <- list()

glm_list$glm_01 <- glm(mpg ~ cyl                 , mtcars, family = gaussian())
glm_list$glm_02 <- glm(mpg ~ cyl + disp          , mtcars, family = gaussian())
glm_list$glm_03 <- glm(mpg ~ cyl + disp + hp     , mtcars, family = gaussian())
glm_list$glm_04 <- glm(mpg ~ cyl + disp + hp + wt, mtcars, family = gaussian())

# create a table to store the pairwise ANOVA results

n_models <- length(glm_list)
anova_table <- matrix(0, nrow = n_models, ncol = n_models)

# loop through twice and do pairwise comparisons

row_idx <- 0
col_idx <- 0

for(row_glm in glm_list)
{
  row_idx <- row_idx + 1
  
  for(col_glm in glm_list)
  {
    col_idx <- col_idx + 1
    anova_table[row_idx, col_idx] <- anova(row_glm, col_glm, test = "F")$'Pr(>F)'[2]
  }
  col_idx <- 0
}
row_idx <- 0

It would be far easier to do if you just put all your models in a list to start with. Then you can just iterate over them using list indices. — Allan Cameron, Aug 25 '20 at 11:05
Ohhh...I hadn't thought about that. Have had a play with the code and edited my posting to show a possible solution (a bit clumsy but it seems to work). Thank you. — Alan, Aug 25 '20 at 11:18

score 2 · Accepted Answer · answered Aug 25 '20 at 11:15

The easiest way to do this would be to keep all your models in a list. This makes it simple to iterate over them. For example, you can create all of your models and do a pairwise comparison between all of them like this:

data(mtcars)

f_list <- list(mpg ~ cyl, 
               mpg ~ cyl + disp,
               mpg ~ cyl + disp + hp,
               mpg ~ cyl + disp + hp + wt)

all_glms  <- lapply(f_list, glm, data = mtcars, family = gaussian)
all_pairs <- as.data.frame(combn(length(all_glms), 2))
result <- lapply(all_pairs, function(i) anova(all_glms[[i[1]]], all_glms[[i[2]]]))

Which gives you:

result
#> $V1
#> Analysis of Deviance Table
#> 
#> Model 1: mpg ~ cyl
#> Model 2: mpg ~ cyl + disp
#>   Resid. Df Resid. Dev Df Deviance
#> 1        30     308.33            
#> 2        29     270.74  1   37.594
#> 
#> $V2
#> Analysis of Deviance Table
#> 
#> Model 1: mpg ~ cyl
#> Model 2: mpg ~ cyl + disp + hp
#>   Resid. Df Resid. Dev Df Deviance
#> 1        30     308.33            
#> 2        28     261.37  2   46.965
#> 
#> $V3
#> Analysis of Deviance Table
#> 
#> Model 1: mpg ~ cyl
#> Model 2: mpg ~ cyl + disp + hp + wt
#>   Resid. Df Resid. Dev Df Deviance
#> 1        30     308.33            
#> 2        27     170.44  3   137.89
#> 
#> $V4
#> Analysis of Deviance Table
#> 
#> Model 1: mpg ~ cyl + disp
#> Model 2: mpg ~ cyl + disp + hp
#>   Resid. Df Resid. Dev Df Deviance
#> 1        29     270.74            
#> 2        28     261.37  1   9.3709
#> 
#> $V5
#> Analysis of Deviance Table
#> 
#> Model 1: mpg ~ cyl + disp
#> Model 2: mpg ~ cyl + disp + hp + wt
#>   Resid. Df Resid. Dev Df Deviance
#> 1        29     270.74            
#> 2        27     170.44  2    100.3
#> 
#> $V6
#> Analysis of Deviance Table
#> 
#> Model 1: mpg ~ cyl + disp + hp
#> Model 2: mpg ~ cyl + disp + hp + wt
#>   Resid. Df Resid. Dev Df Deviance
#> 1        28     261.37            
#> 2        27     170.44  1   90.925

^{Created on 2020-08-25 by the reprex package (v0.3.0)}

Thank you. I did something myself (when I saw your first comment) but it's not quite as elegant as yours (I'm not very good at using *apply in R!). — Alan, Aug 25 '20 at 11:19

score 1 · Answer 2 · answered Aug 25 '20 at 12:18

If you want to reference arbitrary objects in an accessible environment by symbol without putting them into a list object, the standard way to return the top object on the search list whose symbol is equal to a string is get(), or the vector equivalent mget(). I.e. get("glm_01") gets you the top object on the search list that has the symbol glm_01. The most minimal modification to your approach would be to wrap your calls to model_list[row_index] and model_list[col_index] in get().

You can be more precise about where to look for objects by assigning the models in a named environment and only getting from that environment (using the envir parameter to get()).

R - passing object to function based on the NAME of the object

2 Answers2