dotwhisker (version 0.5.0)

dwplot: Dot-and-Whisker Plots of Regression Results

Description

dwplot is a function for quickly and easily generating dot-and-whisker plots of regression models saved in tidy data frames.

Usage

dwplot(x, dodge_size = 0.4, order_vars = NULL, show_intercept = FALSE,
  model_name = "model", style = c("dotwhisker", "distribution"),
  by_2sd = TRUE, vline = NULL, dot_args = list(size = 1.2),
  whisker_args = list(size = 0.5), dist_args = list(alpha = 0.5),
  line_args = list(alpha = 0.75, size = 1), ...)
dw_plot(x, dodge_size = 0.4, order_vars = NULL, show_intercept = FALSE,
  model_name = "model", style = c("dotwhisker", "distribution"),
  by_2sd = TRUE, vline = NULL, dot_args = list(size = 1.2),
  whisker_args = list(size = 0.5), dist_args = list(alpha = 0.5),
  line_args = list(alpha = 0.75, size = 1), ...)

Arguments

x

Either a model object to be tidied with tidy, or a list of such model objects, or a tidy data frame of regression results (see 'Details').

dodge_size

A number indicating how much vertical separation should be between different models' coefficients when multiple models are graphed in a single plot. Lower values tend to look better when the number of independent variables is small, while a higher value may be helpful when many models appear on the same plot; the default is 0.4.

order_vars

A vector of variable names that specifies the order in which the variables are to appear along the y-axis of the plot.

show_intercept

A logical constant indicating whether the coefficient of the intercept term should be plotted.

model_name

The name of a variable that distinguishes separate models within a tidy data frame.

style

Either "dotwhisker" or "distribution". "dotwhisker", the default, shows the regression coefficients' point estimates as dots with confidence interval whiskers. "distribution" shows the normal distribution with mean equal to the point estimate and standard deviation equal to the standard error, underscored with a confidence interval whisker.

by_2sd

When x is model object or list of model objects, should the coefficients for predictors that are not binary be rescaled by twice the standard deviation of these variables in the dataset analyzed, per Gelman (2008)? Defaults to TRUE. Note that when x is a tidy data frame, one can use by_2sd to rescale similarly.

vline

A geom_vline() object, typically with xintercept = 0, to be drawn behind the coefficients.

dot_args

When style is "dotwhisker", a list of arguments specifying the appearance of the dots representing mean estimates. For supported arguments, see geom_point.

whisker_args

When style is "dotwhisker", a list of arguments specifying the appearance of the whiskers representing the confidence intervals. For supported arguments, see geom_linerangeh.

dist_args

When style is "distribution", a list of arguments specifying the appearance of normally distributed regression estimates. For supported arguments, see geom_polygon.

line_args

When style is "distribution", a list of arguments specifying the appearance of the line marking the confidence interval beneath the normal distribution. For supported arguments, see geom_linerangeh.

…

Extra arguments to pass to tidy.

Value

The function returns a ggplot object.

Details

dwplot visualizes regression model objects or regression results saved in tidy data frames by, e.g., tidy as dot-and-whisker plots generated by ggplot.

Tidy data frames to be plotted should include the variables term (names of predictors), estimate (corresponding estimates of coefficients or other quantities of interest), std.error (corresponding standard errors), and optionally model (when multiple models are desired on a single plot; a different name for this last variable may be specified using the model_name argument). In place of std.error one may substitute conf.low (the lower bounds of the confidence intervals of each estimate) and conf.high (the corresponding upper bounds).

For convenience, dwplot also accepts as input those model objects that can be tidied by tidy, or a list of such model objects.

By default, the plot will display 95-percent confidence intervals. To display a different interval when passing a model object or objects, specify a conf.level argument to pass to tidy. When passing a data frame of results, include the variables conf.low and conf.high describing the bounds of the desired interval.

Because the function can take a data frame as input, it is easily employed for a wide range of models, including those not supported by tidy. And because the output is a ggplot object, it can easily be further customized with any additional arguments and layers supported by ggplot2. Together, these two features make dwplot extremely flexible.

References

Kastellec, Jonathan P. and Leoni, Eduardo L. 2007. "Using Graphs Instead of Tables in Political Science." Perspectives on Politics, 5(4):755-771.

Gelman, Andrew. 2008. "Scaling Regression Inputs by Dividing by Two Standard Deviations." Statistics in Medicine, 27:2865-2873.

Examples

# NOT RUN {
library(broom)
library(dplyr)
# Plot regression coefficients from a single model object
data(mtcars)
m1 <- lm(mpg ~ wt + cyl + disp, data = mtcars)
dwplot(m1, vline = geom_vline(xintercept = 0, colour = "grey50", linetype = 2)) +
    xlab("Coefficient")
# using 99% confidence interval
dwplot(m1, conf.level = .99)
# Plot regression coefficients from multiple models
m2 <- update(m1, . ~ . - disp)
dwplot(list(full = m1, nodisp = m2))
# Change the appearance of dots and whiskers
dwplot(m1, dot_args = list(size = 3, pch = 21, fill = "white"))
# Plot regression coefficients from multiple models on the fly
mtcars %>%
    split(.$am) %>%
    purrr::map(~ lm(mpg ~ wt + cyl + disp, data = .x)) %>%
    dwplot() %>%
    relabel_predictors(c(wt = "Weight", cyl = "Cylinders", disp = "Displacement")) +
    theme_bw() + xlab("Coefficient") + ylab("") +
    geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +
    ggtitle("Predicting Gas Mileage, OLS Estimates") +
    theme(plot.title = element_text(face = "bold"),
          legend.position = c(.995, .99),
          legend.justification = c(1, 1),
          legend.background = element_rect(colour="grey80"),
          legend.title.align = .5) +
    scale_colour_grey(start = .4, end = .8,
                      name = "Transmission",
                      breaks = c("Model 0", "Model 1"),
                      labels = c("Automatic", "Manual"))

# }