Problem extracting model covariates for model summary table - r

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
Models end up being misnumbered
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)