small_multiple {dotwhisker} R Documentation

## Generate a 'Small Multiple' Plot of Regression Results

### Description

small_multiple is a function for plotting regression results of multiple models as a 'small multiple' plot

### Usage

small_multiple(
x,
ci = 0.95,
margins = FALSE,
dodge_size = 0.4,
show_intercept = FALSE,
model_order = NULL,
submodel_order = NULL,
axis_switch = FALSE,
by_2sd = FALSE,
dot_args = list(size = 0.3),
...
)


### 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'). ci A number indicating the level of confidence intervals; the default is .95. margins A logical value indicating whether presenting the average marginal effects of the estimates. See the Details for more information. dodge_size A number (typically between 0 and 0.3; the default is .06) indicating how much horizontal separation should appear between different submodels' coefficients when multiple submodels are graphed in a single plot. Lower values tend to look better when the number of models is small, while a higher value may be helpful when many submodels appear on the same plot. show_intercept A logical constant indicating whether the coefficient of the intercept term should be plotted model_order A character vector defining the order of the models when multiple models are involved. submodel_order A character vector defining the order of the submodels when multiple submodels are involved. axis_switch A logical constant indicating the position of variable labels and y axis ticks. Default is FALSE, when the variable label is on the right side, and y axis ticks is on the left size. 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. dot_args A list of arguments specifying the appearance of the dots representing mean estimates. For supported arguments, see geom_pointrangeh. ... Arguments to pass to dwplot.

### Details

small_multiple, following Kastellec and Leoni (2007), provides a compact means of representing numerous regression models in a single plot.

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 model (identifying the corresponding model). 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).

Alternately, small_multiple accepts as input a list of model objects that can be tidied by tidy (or tidy_parameters, parameters (with proper formatting)), or a list of such model objects.

Optionally, more than one set of results can be clustered to facilitate comparison within each model; one example of when this may be desirable is to compare results across samples. In that case, the data frame should also include a variable submodel identifying the submodel of the results.

### Value

The function returns a ggplot object.

### References

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

### Examples

library(broom)
library(dplyr)

# Generate a tidy data frame of regression results from six models

m <- list()
ordered_vars <- c("wt", "cyl", "disp", "hp", "gear", "am")
m[[1]] <- lm(mpg ~ wt, data = mtcars)
m123456_df <- m[[1]] %>% tidy %>% by_2sd(mtcars) %>%
mutate(model = "Model 1")

for (i in 2:6) {
m[[i]] <- update(m[[i-1]], paste(". ~ . +", ordered_vars[i]))
m123456_df <- rbind(m123456_df, m[[i]] %>% tidy %>% by_2sd(mtcars) %>%
mutate(model = paste("Model", i)))
}

# Generate a 'small multiple' plot
small_multiple(m123456_df)

## Using submodels to compare results across different samples
# Generate a tidy data frame of regression results from five models on
# the mtcars data subset by transmission type (am)
ordered_vars <- c("wt", "cyl", "disp", "hp", "gear")
mod <- "mpg ~ wt"
by_trans <- mtcars %>% group_by(am) %>%  # group data by transmission
do(tidy(lm(mod, data = .))) %>%        # run model on each group
rename(submodel = am) %>%              # make submodel variable
mutate(model = "Model 1") %>%          # make model variable
ungroup()

for (i in 2:5) {
mod <- paste(mod, "+", ordered_vars[i])
by_trans <- rbind(by_trans, mtcars %>% group_by(am) %>%
do(tidy(lm(mod, data = .))) %>%
rename(submodel = am) %>%
mutate(model = paste("Model", i)) %>%
ungroup())
}

small_multiple(by_trans) +
theme_bw() + ylab("Coefficient Estimate") +
geom_hline(yintercept = 0, colour = "grey60", linetype = 2) +
theme(axis.text.x  = element_text(angle = 45, hjust = 1),
legend.position=c(0, 0), legend.justification=c(0, 0),
legend.title = element_text(size=9),
legend.background = element_rect(color="gray90"),
legend.spacing = unit(-3, "pt"),
legend.key.size = unit(10, "pt")) +
scale_colour_hue(name = "Transmission",
breaks = c(0, 1),
labels = c("Automatic", "Manual"))



[Package dotwhisker version 0.7.4 Index]