自动变量选择–回归线性模型

Question

In the MWE below, I have a data set with 70 potential predictors to explain my variable price1 . I would like to do univariate analysis with all the variables but the package glmulti says that I have too many predictors . How a univariate analysis can have too many predictors?

*I could do it by means of a loop / apply but I am looking for something more elaborated. This similar question here doesn't solve the question either.

test <- read.csv(url("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/Ecdat/Car.csv"))
library(glmulti)
glmulti.lm.out <- glmulti(data  = test, price1 ~ .,
                          level = 1,
                          method = "h",
                          maxK = 1,
                          confsetsize = 10,
                          fitfunction = "lm")

Error
Warning message:
In glmulti(y = "price1", data = test, level = 1, maxK = 1, method = "h",  :
  !Too many predictors.

Answer 1

This question is more geared for CrossValidated, but here's my two cents. Running an exhaustive search to find the best variables to include in a model is very computationally heavy and gets out of hand really quickly. Consider what you're asking the computer to do:

When you're running an exhaustive search, the computer is building a model for every possible combination of variables. For a model of size one, that's not too bad because that's only 70 models. But even for a two variable model, the computer has to run n!/(r!(nr)!) = 70!/(2!(68)!) = 2415 different models. Things spiral out of control from there.

As a work-around, I'll point you to the leaps package, which has the regsubsets function. Then, you can run either a Forward or a Backward subset selection model and find the most important variables in a step-wise manner. After running each, you may be able to toss out the variables that are omitted from each and run your model with fewer predictors using glmulti , but no promises.

test.data <-
read.csv(url("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/ma
ster/csv/Ecdat/Car.csv"))[,2:71]
library(leaps)

big_subset_model <- regsubsets(x = price1 ~ ., data = test.data, nbest = 1, 
method = "forward", really.big = TRUE, nvmax = 70)
sum.model <- summary(big_subset_model)

Answer 2

A simple solution for univariate analysis using lapply.

test <- read.csv(url("https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/Ecdat/Car.csv")) 

reg <- function(indep_var,dep_var,data_source) {
          formula <- as.formula(paste(dep_var," ~ ", indep_var))
          res     <- lm(formula, data = data_source)
          summary(res)
}

lapply(colnames(test), FUN = reg, dep_var = "price1", data_source = test)