The following examples demonstrate how to use plot_me from the plotMElm version >= 0.1.4) R package to visually assess and control the false positive rate issues raised by Esarey and Sumner. These examples build on the examples in their interactionTest R package.

False Discovery Rate CIs vs. Standard CIs

library(plotMElm)
library(interactionTest)
library(ggplot2)
library(gridExtra)

# Load Clark and Golder (2006) replication data
data(legfig) 

# Limit to established democracies from the 1990s
dat <- subset(legfig, subset = (nineties == 1 & old == 1))

# Estimate model
lin_formula <- enep1 ~ eneg * logmag + eneg * uppertier +
                  proximity1 * enpres
lin_mod <- lm(lin_formula, data = dat)

# Marginal effects with 'standard' and FDR 95% confidence intervals
p1 <- plot_me(lin_mod, term1 = 'eneg', term2 = 'logmag') + ggtitle('Standard CI')
p2 <- plot_me(lin_mod, term1 = 'eneg', term2 = 'logmag', ci_type = 'fdr') +
                ggtitle('FDR CI')
## t-statistic used: 3.385
grid.arrange(p1, p2, nrow = 1)

Custom t-statistic

It is also possible to supply custom t-statistics with the t_statistic argument. This is useful if you want to use a funciton like findMultiLims from the interactionTest to find t-statistics that can be used to correct confidence intervals for under-confidence. For example:

library(boot)

# Create bootstrap samples of marginal effects of eneg and logmag on enep1
boot_t_dist <- boot(data = dat, statistic = bootFun, R = 1000,
                    form = lin_formula, fam = 'gaussian', 
                    x.name = 'eneg', z.name = 'logmag')$t

boot_t_x_dist <- boot_t_dist[, 1:10]

# Calculate critical t-statistic that sets familywise error rate to 10%
# for statistical significance of marginal effect of of eneg at any value of logmag
custom_t <- findMultiLims(boot_t_x_dist, type = 'any', err = 0.1)$mi

# Use custom t-statistic
plot_me(lin_mod, term1 = 'eneg', term2 = 'logmag', t_statistic = custom_t) + 
        ggtitle(sprintf('CI from t-statistic = %s', round(custom_t, digits = 3)))
## Using custom t-statistic (ignoring ci_type argument).