plotMElm
A simple R package to plot marginal effects from interactions estimated from linear models.
## 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 variable
mtcars$cyl <- factor(mtcars$cyl,
labels = c('4 Cyl', '6 Cyl', '8 Cyl'))
# Estimate model
m2 <- lm(mpg ~ wt * cyl, data = mtcars)
# Plot marginal effect of Weight across the Number of Cylinders
plot_me(m2, 'wt', 'cyl')
Note that point ranges will also be used if there are five or fewer fitted values.
Determining the False Positive Rate
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 intervals
plot_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
package to find t-statistics that can be used to correct confidence
intervals for underconfidence.
See also
The interplot package also has some of the same capabilities as plotMElm.