| fdrInteraction {interactionTest} | R Documentation |
Critical t-statistic
Description
This function calculates the critical t-statistic to limit the false discovery rate (Benjamini and Hochberg 1995) for a marginal effects plot to a specified level.
Usage
fdrInteraction(me.vec, me.sd.vec, df, type = "BH", level = 0.95)
Arguments
me.vec |
A vector of marginal effects. |
me.sd.vec |
A vector of standard deviations for the marginal effects. |
df |
Degrees of freedom. |
type |
Should the BH (Benjamini and Hochberg 1999) or BY (Benjamini and Yekutieli 2000) correction be used? Options are "BH" (the default) or "BY". |
level |
The level of confidence. Defaults to 0.95. |
Value
The critical t-statistic for the interaction.
Author(s)
Justin Esarey and Jane Lawrence Sumner
References
Benjamini, Yoav, and Yosef Hochberg. 1995. "Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing." Journal of the Royal Statistical Society, Series B 57(1): 289-300.
Benjamini, Yoav, and Daniel Yekutieli. 2001. "The Control of the False Discovery Rate in Multiple Testing Under Dependency." The Annals of Statistics 29(4): 1165-1188.
Clark, William R., and Matt Golder. 2006. "Rehabilitating Duverger's Theory." Comparative Political Studies 39(6): 679-708.
Esarey, Justin, and Jane Lawrence Sumner. 2017. "Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate." Comparative Political Studies 51(9): 1144-1176.
Esarey, Justin, and Jane Lawrence Sumner. 2018. "Corrigendum to 'Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate.'"
Examples
## Not run:
data(legfig) # Clark and Golder 2006 replication data
# limit to established democracies from the 1990s
dat<-subset(legfig, subset=(nineties==1 & old==1))
lin.mod <- lm(enep1 ~ eneg + logmag + logmag_eneg + uppertier_eneg + uppertier +
proximity1 + proximity1_enpres + enpres, data=dat)
# save betas
beta.mod <- coefficients(lin.mod)
# save vcv
vcv.mod <- vcov(lin.mod)
# calculate MEs
mag <- seq(from=0.01, to=5, by=0.01)
me.vec <- beta.mod[2] + beta.mod[4]*mag
me.se <- sqrt( vcv.mod[2,2] + (mag^2)*vcv.mod[4,4] + 2*(mag)*(vcv.mod[2,4]) )
ci.hi <- me.vec + 1.697 * me.se
ci.lo <- me.vec - 1.697 * me.se
plot(me.vec ~ mag, type="l", ylim = c(-4, 6))
lines(ci.hi ~ mag, lty=2)
lines(ci.lo ~ mag, lty=2)
fdrInteraction(me.vec, me.se, df=lin.mod$df, level=0.90) # 4.233986
ci.hi <- me.vec + 4.233986 * me.se
ci.lo <- me.vec - 4.233986 * me.se
lines(ci.hi ~ mag, lty=2, lwd=2)
lines(ci.lo ~ mag, lty=2, lwd=2)
abline(h=0, lty=1, col="gray")
legend("topleft", lwd=c(1,2), lty=c(1,2), legend=c("90% CI", "90% FDR CI"))
## End(Not run)