Plot the impact for each variable in linear regression?-CodePudding

I want to create a plot like below for a lm model calculated using R.

Is there a simple way of doing it?

The plot above was collected here in this page.

CodePudding user response：

Package {caret} offers a convenient method varImp: Example:

library(caret)
my_model <- lm(mpg ~ disp   cyl, data = mtcars)

## > varImp(my_model)
##
##       Overall
## disp 2.006696
## cyl  2.229809

For different measures of variable importance see ?varImp. Feed values into your plotting library of choice.

CodePudding user response：

Following the method in the linked article (relative marginal increase in r squared), you could write your own function that takes a formula, and the data frame, then plots the relative importance:

library(ggplot2)

plot_importance <- function(formula, data) {
  
  lhs <- as.character(as.list(formula)[[2]])
  rhs <- as.list(as.list(formula)[[3]])
  vars <- grep("[ \\*]", rapply(rhs, as.character), invert = TRUE, value = TRUE)
  df <- do.call(rbind, lapply(seq_along(vars), function(i) {
    f1 <- as.formula(paste(lhs, paste(vars[-i], collapse = " "), sep = "~"))
    f2 <- as.formula(paste(lhs, paste(c(vars[-i], vars[i]), collapse = " "), 
                           sep = "~"))
    r1 <- summary(lm(f1, data = data))$r.squared
    r2 <- summary(lm(f2, data = data))$r.squared
    
    data.frame(variable = vars[i], importance = r2 - r1)
  }))
  
  df$importance <- df$importance / sum(df$importance)
  df$variable <- reorder(factor(df$variable), -df$importance)

  ggplot(df, aes(x = variable, y = importance))  
    geom_col(fill = "deepskyblue4")  
    scale_y_continuous(labels = scales::percent)  
    coord_flip()  
    labs(title = "Relative importance of variables",
         subtitle = deparse(formula))  
    theme_classic(base_size = 16)
}

We can test this out with the sample data provided in the linked article:

IV <- read.csv(paste0("https://statisticsbyjim.com/wp-content/uploads/",
                      "2017/07/ImportantVariables.csv"))

plot_importance(Strength ~ Time   Pressure   Temperature, data = IV)

And we see that the plot is the same.

We can also test it out on some built-in datasets to demonstrate that its use is generalized:

plot_importance(mpg ~ disp   wt   gear, data = mtcars)

plot_importance(Petal.Length ~ Species   Petal.Width, data = iris)

^{Created on 2022-05-01 by the reprex package (v2.0.1)}

CodePudding user response：

Just ended up using relaimpo package and showing with ggplot answered by @Allan Cameron

library(relaimpo)

relative_importance <- calc.relimp(mymodel, type="lmg")$lmg

df = data.frame(
variable=names(relative_importance),
importance=round(c(relative_importance) * 100,2)
)

ggplot(df, aes(x = reorder(variable, -importance), y = importance))  
  geom_col(fill = "deepskyblue4")   
  geom_text(aes(label=importance), vjust=.3, hjust=1.2, size=3, color="white") 
  coord_flip()  
  labs(title = "Relative importance of variables")  
  theme_classic(base_size = 16)