Drop each Regressor step by step - r

Question

Is there any chance to specify the full model once and then just to drop regressors one after the other and producing a nice stargazer table with it without having to write every regression line again and again?
data <- datasets::airquality
# Treating Month and Day as crosssectional and time fixed effects
re1 <- plm(data = data, Ozone~Solar.R+Wind+Temp,
index=c("Month", "Day"), model="within", effect="twoways") # full model
# this is the only regression line I actually want to write.
# The other regressors should be automatically dropped one by one in subsequent regressions.
re2 <- plm(data = data, Ozone~Wind+Temp,
index=c("Month", "Day"), model="within", effect="twoways") # first regressor dropped
re3 <- plm(data = data, Ozone~Solar.R+Temp,
index=c("Month", "Day"), model="within", effect="twoways") # second regressor dropped
re4 <- plm(data = data, Ozone~Solar.R+Wind,
index=c("Month", "Day"), model="within", effect="twoways") # third regressor dropped
stargazer(re1, re2, re3, re4,
type = "html",
title = "Dropped after one another",
out="HopeThisWorks.html")
I have looked into the step() function but this isn't quite helping as I am not aiming to drop according to significance or anything.

Answer 1

re1 <- plm(data = data, Ozone~Solar.R+Wind+Temp,
index=c("Month", "Day"), model="within", effect="twoways") # full model
A=lapply(paste0(".~.-",c("Solar.R","Wind","Temp")),function(x)update(re1,as.formula(x)))
[[1]]
Model Formula: Ozone ~ Wind + Temp
Coefficients:
Wind Temp
-2.6933 2.3735
[[2]]
Model Formula: Ozone ~ Solar.R + Temp
Coefficients:
Solar.R Temp
0.040986 2.782978
[[3]]
Model Formula: Ozone ~ Solar.R + Wind
Coefficients:
Solar.R Wind
0.096607 -4.841992
Now to be able to access this in the global environment: use
list2env(setNames(A,paste0("re",seq_along(A)+1)),.GlobalEnv)
stargazer(re1, re2, re3, re4,
type = "html",
title = "Dropped after one another",
out="HopeThisWorks.html")

Answer 2

This is a more flexible alternative:
bene <- function(data = datasets::airquality,
depvar = "Ozone",
covariates = c("Wind","Temp","Solar.R")){
funny <- function(id){
covs <- ifelse(is.na(id),paste0(covariates,collapse = " + "),paste0(covariates[-id],collapse = " + "))
model <- eval(parse(text=paste0("plm(data = data,",depvar," ~ ",covs,",index=c('Month', 'Day'), model='within', effect='twoways')")))
return(model)
}
xx <- capture.output(stargazer::stargazer(purrr::map(c(NA,1:length(covariates)),funny),
type = "html",
out = paste0("results/model.html"),
star.cutoffs = c(0.05,0.01,0.001),
title = paste0(depvar)))
models <- purrr::map(c(NA,1:length(covariates)),funny)
map(models,function(x)print(summary(x)))
}
bene(data = datasets::airquality,
depvar = "Ozone",
covariates = c("Wind","Temp","Solar.R"))

Answer 3

You could use (an adaption of) this (so far only stepwise-working) function, which shows you the stargazer output and also saves the regression table as a html file.
stepwise_model <- function(data = "datasets::airquality",
depvar = "Ozone",
covariates = c("Wind","Temp","Solar.R")){
data_df <- eval(parse(text = data))
models <- c()
for(q in 1:length(covariates)){
label <- paste0("mod_",depvar,"_",q)
models[q] <- label
cov <- paste0(covariates[1:q],collapse = " + ")
eval(parse(text = paste0(label," <<- plm::plm(",depvar," ~ ",cov,",data = data_df,index=c('Month', 'Day'), model='within', effect='twoways')")))
eval(parse(text = paste0("print(summary(",label,"))")))
}
modellist <- eval(parse(text = paste0("list(",paste0(models,collapse = ","),")")))
xx <- capture.output(stargazer::stargazer(modellist ,
type = "html",
out = paste0("results/paper/models/mod_",depvar,".html"),
star.cutoffs = c(0.05,0.01,0.001),
title = paste0(depvar)))
}
stepwise_model()