#> Loading required package: ggplot2
#> Registered S3 method overwritten by 'GGally':
#>   method from   
#>   ggplot2


The purpose of this function is to quickly plot the coefficients of a model.

Quick coefficients plot

To work automatically, this function requires the broom package. Simply call ggcoef() with a model object. It could be the result of stats::lm, stats::glm or any other model covered by broom and its broom::tidy method1.

reg <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data = iris)

In the case of a logistic regression (or any other model for which coefficients are usually exponentiated), simply indicated exponentiate = TRUE. Note that a logarithmic scale will be used for the x-axis.

d <-
log.reg <- glm(Survived ~ Sex + Age + Class, family = binomial, data = d, weights = d$Freq)
ggcoef(log.reg, exponentiate = TRUE)

Customizing the plot

You can use, vline and exclude_intercept to display or not confidence intervals as error bars, a vertical line for x = 0 (or x = 1 if coefficients are exponentiated) and the intercept.

ggcoef(reg, vline = FALSE, = FALSE, exclude_intercept = TRUE)

See the help page of ggcoef() for the full list of arguments that could be used to personalize how error bars and the vertical line are plotted.

  exponentiate = TRUE,
  vline_color = "red",
  vline_linetype =  "solid",
  errorbar_color = "blue",
  errorbar_height = .25

Additional parameters will be passed to [ggplot2::geom_point()].

ggcoef(log.reg, exponentiate = TRUE, color = "purple", size = 5, shape = 18)

Finally, you can also customize the aesthetic mapping of the points.

ggcoef(log.reg, exponentiate = TRUE, mapping = aes(x = estimate, y = term, size = p.value)) +
  scale_size_continuous(trans = "reverse")

Custom data frame

You can also pass a custom data frame to [ggcoef()]. The following variables are expected:

  • term (except if you customize the mapping)
  • estimate (except if you customize the mapping)
  • conf.low and conf.high (only if you want to display error bars)
cust <- data.frame(
  term = c("male vs. female", "30-49 vs. 18-29", "50+ vs. 18-29", "urban vs. rural"),
  estimate = c(.456, 1.234, 1.897, 1.003),
  conf.low = c(.411, 1.042, 1.765, 0.678),
  conf.high = c(.498, 1.564, 2.034, 1.476),
  variable = c("sex", "age", "age", "residence")
cust$term <- factor(cust$term, cust$term)
ggcoef(cust, exponentiate = TRUE)

  exponentiate = TRUE,
  mapping = aes(x = estimate, y = term, colour = variable),
  size = 5