Plot marginal effects for interactions estimated from linear models.
A simple R package to plot marginal effects from interactions estimated from linear models.
The package contains one simply function:
plot_me for plotting marginal
effects from interactions estimated from models estimated with the
lm function in base R. For example, when the second term is continuous:
# Load packagelibrary(plotMElm)# Estimate modelstates <- as.data.frame(state.x77)m1 <- lm(Murder ~ Income * Population, data = states)# Plot marginal effect of Income across the observed range of Populationplot_me(m1, 'Income', 'Population')
## Categorical (Factor) Term 2
When the second term in the interaction is a categorical (factor) variable then point-ranges are plotted. Note that the marginal effect is in terms of the reference category:
# Set Term 2 as a factor variablemtcars$cyl <- factor(mtcars$cyl,labels = c('4 Cyl', '6 Cyl', '8 Cyl'))# Estimate modelm2 <- lm(mpg ~ wt * cyl, data = mtcars)# Plot marginal effect of Weight across the Number of Cylindersplot_me(m2, 'wt', 'cyl')
Note that point ranges will also be used if there are five or fewer fitted values.
Esarey and Sumner
show that pointwise confidence intervals from marginal effect plots produce
statistically significant findings at a rate that can be larger or smaller
than is warrented.
plot_me allows users to specify
ci_type = 'fdr' to find
confidence intervals that correct for overly confident marginal effects in the
face of multiple comparisons. FDR stands for "False Discovery Rate". For example:
# Plot marginal effect of Income across the observed range of Population# with false discovery rate limited confidence intervalsplot_me(m1, 'Income', 'Population', ci_type = 'fdr')
Here is the result compared with standard confidence intervals:
## t-statistic used: 2.269
You can also use the
t_statistic argument to supply custom t-statistics
for creating the marginal effect confidence intervals. This is useful if you
want to use a funciton like
findMultiLims from the
interactTest to find t-statistics
that can be used to correct confidence intervals for underconfidence.
The interplot package also has some of the same capabilities as plotMElm.
Allow user to find false discovery rate limiting confidence intervals with
Allow the user to specify custom t-statistics for finding the confidence intervals.
Allow any confidence level with
Return a data frame instead of a plot with
Thanks to Vincent Arel-Bundock for both contributions.