Problem extracting model covariates for model summary table

Question

I'm a graduate student using a linear regression (count) model to understand drivers of fish movement into and out of tidal wetlands. I am currently trying to generate a publication-worthy model summary table in r. I've been using the sel.table function which has been working well for this purpose.

However, I've been unable to generate a column that contains the individual model formulas. Below is my code which is based off of some nice instructions for using the MuMIn package. https://sites.google.com/site/rforfishandwildlifegrads/home/mumin_usage_examples

So to recap, my question pertains to the last line of code below,

How can I insert model formulas into a model selection table.**

install.packages("MuMIn")
library(MuMIn)

data = mtcars

models = list(
  model1 <- lm(mpg ~ cyl, data = data),
  model2 <- lm(mpg ~ cyl + hp, data = data),
  model3 <- lm(mpg ~ cyl * hp, data = data)
)

#create an object “out.put” that contains all of the model selection information
out.put <- model.sel(models)

#coerce the object out.put into a data frame
sel.table <-as.data.frame(out.put)[6:10]

#add a column for model names
sel.table$Model <- rownames(sel.table)

#replace model name with formulas
for(i in 1:nrow(sel.table)) sel.table$Model[i]<- as.character(formula(paste(sel.table$Model[i])))[3]

#Any help on this topic would be greatly appreciated!

UPDATED CODE

My method of pulling out model names is pretty clunky but otherwise this code seems to generate what I intended (a complete model selection table). Also, I'm not sure if the model coefficients are displayed properly but I hope to follow up on this for my final answer.

data = mtcars

#write linear models
models = list(
  model1 <- lm(mpg ~ cyl, data = data),
  model2 <- lm(mpg ~ cyl + hp, data = data),
  model3 <- lm(mpg ~ cyl * hp + disp, data = data),
  model4 <- lm(mpg ~ cyl * hp + disp + wt + drat, data = data)
)

#create an object “out.put” that contains all of the model selection information
out.put <- model.sel(models)

#coerce the object out.put into a data frame
sel.table <-as.data.frame(out.put)

#slightly rename intercept column
names(sel.table)[1]="Intercept"

#select variables to display in model summary table
sel.table <- sel.table %>% 
 select(Intercept,cyl,hp,disp,wt,drat,df,logLik,AICc,delta)

#round numerical coumns
sel.table[,1:6]<- round(sel.table[,1:6],2)

sel.table[,8:10]<-round(sel.table[,8:10],2)

#add a column for model (row) names
sel.table$Model <- rownames(sel.table)

#extract model formulas
form <- data.frame(name = as.character(lapply(models, `[[`, c(10,2))))

#generate a column with model (row) numbers (beside associated model formulas)
form <- form %>% 
  mutate(Model=(1:4))

#merge model table and model formulas
sum_table <- merge (form,sel.table,by="Model")

#rename model equation column 
names(sum_table)[2]="Formula"

print <- flextable(head(sum_table))
print <- autofit(print)
print

6/1/20 UPDATE:

Below is an image that describes two issues that I'm having with the code. I've found a workaround to the first question but I'm still investigating the second. see details here

1. Models end up being misnumbered
2. Model formula columns are being generated for each model

Answer 1

I believe there is a part of the code missing in the examples you followed, that is why your code does not work.

The easiest way to generate formula-like strings is simply to deparse the right hand side of the model formulas (i.e. 3-rd element):

sapply(get.models(out.put, TRUE), function(mo) deparse(formula(mo)[[3]], width.cutoff = 500))

or, if you want A*B's expanded into A + B + A:B:

sapply(get.models(out.put, TRUE), function(mo) deparse(terms(formula(mo), simplify = TRUE)[[3]], width.cutoff = 500))

Update: the original example code improved and simplified:

library(MuMIn)

data <- mtcars

#! Feed the models directly to `model.sel`. No need to create a separate list of
#! models.
gm <- lm(mpg ~ cyl, data = data)
out.put <- model.sel(
  model1 = gm,
  model2 = update(gm, . ~. + hp),
  model3 = update(gm, . ~ . * hp + disp),
  model4 = update(gm, . ~ . * hp + disp + wt + drat)
  )

sel.table <- out.put
sel.table$family <- NULL
sel.table <- round(sel.table, 2)
#! Use `get.models` to get the list of models in the same order as in the
#! selection table
sel.table <- cbind(
    Model = 
#! Update (2): model number according to their original order, use:
       attr(out.put, "order"),
#!     otherwise: seq(nrow(sel.table)),
#!
#! Update (2): add a large `width.cutoff` to `deparse` so that the result is
#!         always a single string and `sapply` returns a character vector
#!         rather than a list.
#!         For oversize formulas, use `paste0(deparse(...), collapse = "")`  
    formula = sapply(get.models(out.put, TRUE),
        function(mo) deparse(formula(mo)[[3]], width.cutoff = 500)),
#!
    sel.table
    )

Answer 2

library(MuMIn)

data <- mtcars

#! Feed the models directly to `model.sel`. No need to create a separate list of
#! models.
gm <- lm(mpg ~ cyl, data = data)
out.put <- model.sel(
  model1 = gm,
  model2 = update(gm, . ~. + hp),
  model3 = update(gm, . ~ . * hp + disp),
  model4 = update(gm, . ~ . * hp + disp + wt + drat)
)

sel.table <- out.put
sel.table$family <- NULL
sel.table <- round(sel.table, 2)
#! Use `get.models` to get the list of models in the same order as in the
sel.table <- cbind(
  Model = 
    #! Update (2): model number according to their original order, use:
    attr(out.put, "order"),
  #!     otherwise: seq(nrow(sel.table)),
  #!
  #! Update (2): add a large `width.cutoff` to `deparse` so that the result is
  #!         always a single string and `sapply` returns a character vector
  #!         rather than a list.
  #!         For oversize formulas, use `paste0(deparse(...), collapse = "")`  
  formula = sapply(get.models(out.put, TRUE),
                   function(mo) deparse(formula(mo)[[3]], width.cutoff = 500)),
  #!
  sel.table
)

#slightly rename intercept column  
colnames(sel.table)[3] <- 'Intercept' 

# #select summary columns for model selection table
# sel.table <- sel.table %>%
#   select(Model,formula,Intercept,df,logLik,AICc,delta,weight)

print <- flextable(head(sel.table))
print <- autofit(print)
print

Answer 3

Since your question isn't reproducible, i'll try with something else and maybe that's what you're looking for:

data = mtcars

models = list(
  model1 = lm(mpg ~ cyl, data = data),
  model2 = lm(mpg ~ cyl + hp, data = data)
  )

data.frame(name = as.character(lapply(models, `[[`, c(10,2))),
           other.column = NA)
#>             name other.column
#> 1      mpg ~ cyl           NA
#> 2 mpg ~ cyl + hp           NA

^{Created on 2020-05-28 by the reprex package (v0.3.0)}

The formula (call) of a lm object is on position 10 of the list. You can actually count when you type model1$. You can use rownames() instead of a column, but that's not recommended.

EDIT AFTER REPRODUCIBLE EXAMPLE

library(MuMIn)

data = mtcars

models = list(
  model1 <- lm(mpg ~ cyl, data = data),
  model2 <- lm(mpg ~ cyl + hp, data = data),
  model3 <- lm(mpg ~ cyl * hp, data = data)
)

# create an object that contains all of the model selection information
out.put <- model.sel(models)

#coerce the object out.put into a data frame
sel.table <-as.data.frame(out.put)[6:10]

# formulas as names
sel.table$name = as.character(lapply(models, `[[`, c(10,2)))

# reordering
sel.table = sel.table[, c(6,1,2,3,4,5)]

sel.table
#>             name df    logLik     AICc    delta    weight
#> 3      mpg ~ cyl  5 -78.14329 168.5943 0.000000 0.5713716
#> 1 mpg ~ cyl + hp  3 -81.65321 170.1636 1.569298 0.2607054
#> 2 mpg ~ cyl * hp  4 -80.78092 171.0433 2.449068 0.1679230

^{Created on 2020-05-31 by the reprex package (v0.3.0)}