rflextablegtformattablemodelsummary

Table of models with standardized coefficient size shown using color and size

I'm trying to produce a table of multiple models with standardized coefficients color-coded and sized based on the coefficient size. I'll be doing this with dozens of models and it seems using color and size would be a way to show patterns across predictors and models. Something like this from {corrplot} is what I'm interested in, but I'd only need one color: example

I'm also interested in having cells for p-values > .05 to be blank or made very faint or something.

Here is an example I'm working with.

library(dplyr)
library(modelsummary)

dat <- mtcars %>% mutate(
  cyl = as.factor(cyl),
  gear = as.factor(gear)
)

## List of models
models = list(
  "MPG" = lm(mpg ~ cyl + hp + wt + qsec + vs + am + gear + carb, data = dat),
  "Disp" = lm(disp ~ cyl + hp + wt + qsec + vs + am + gear + carb, data = dat),
  "Drat" = lm(drat ~ cyl + hp + wt + qsec + vs + am + gear + carb, data = dat)
    )

## I feed the list of models to modelsummary() and ask for only coefficients and p.value as side by side columns
models.ep <-  modelsummary(models,
             standardize = "basic", 
             shape = term ~ model + statistic,
             estimate = "{estimate}",
             statistic = "p.value" ,
             gof_map = NA,
             output = "data.frame")

## I started trying to use {formattable} but the color bars aren't what I want (I'm interested in the image shown above with the size and darkness/lightness of the circles representing effect magnitude.  
library(formattable)
formattable(models.ep, list(
"MPG / Est." = color_bar("#e9c46a"),
"Disp / Est." = color_bar("#80ed99"),
"Drat / Est." = color_bar("#f28482")
                             ))

## I also looked around in the {flextable} but did not see a way to do what I need.
Solution

  • You may be able to achieve something similar to this with the get_estimates() function from modelsummary and the ggplot2 package. This is not exactly the image you gave, but it may help you get started:

    library(ggplot2)
library(modelsummary)

dat <- mtcars |> transform(
  cyl = as.factor(cyl),
  gear = as.factor(gear)
)

models = list(
  "MPG" = lm(mpg ~ cyl + hp + wt + qsec + vs + am + gear + carb, data = dat),
  "Disp" = lm(disp ~ cyl + hp + wt + qsec + vs + am + gear + carb, data = dat),
  "Drat" = lm(drat ~ cyl + hp + wt + qsec + vs + am + gear + carb, data = dat)
    )

results <- lapply(models, get_estimates)
results <- lapply(names(results), \(n) transform(results[[n]], model = n))
results <- do.call("rbind", results)

ggplot(results, aes(x = model, y = term, size = estimate, color = p.value)) +
    geom_point() +
    theme_minimal() +
    theme(panel.grid = element_blank()) +
    labs(x = "", y = "")