Package 'interactionTest'

Title: Calculates Critical Test Statistics to Control False Discovery Rates in Marginal Effects Plots
Description: Implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.
Authors: Justin Esarey and Jane Lawrence Sumner
Maintainer: Justin Esarey <[email protected]>
License: GPL
Version: 1.2
Built: 2024-12-19 06:36:27 UTC
Source: CRAN

Help Index


Bootstrapping t-statistics

Description

This function is defunct.

Usage

bootFun(...)

Arguments

...

Any argument to the function (ignored).

References

Esarey, Justin, and Jane Lawrence Sumner. 2018. "Corrigendum to 'Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate.'"


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)

Determine Critical t-Statistic For Marginal Effects Plot

Description

This function is defunct.

Usage

findMultiLims(...)

Arguments

...

Any argument to the function (ignored).

References

Esarey, Justin, and Jane Lawrence Sumner. 2018. "Corrigendum to 'Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate.'"


Replication data for Clark and Golder (2006)

Description

District magnitude and ethnic heterogeneity data from a pooled sample of established democracies in the 1990s. Data originally from Clark and Golder (2006).

Format

A data frame with 754 rows and 33 variables:

country

country name

countrynumber

country number

year

year of observation

enep1

electoral parties

eneg

ethnic heterogeneity

logmag

district magnitude

legelec

legislative election

preselec

presidential election

regime

regime as of 31 Dec of given year (0=democracy, 1=dictatorship)

regime_leg

regime type at time of leg. election (0=democracy, 1=dictatorship)

eighties

election in 1980s closest to 1985

nineties

election in 1990s closest to 1995

old

elections in countries that did not transition to democracy in 1990s

avemag

average district magnitude

districts

number of electoral districts

enep

effective number of ethnic groups fearon

enep_others

n/a

enpp

parliamentary parties - uncorrected

enpp_others

n/a

enpp1

parliamentary parties - corrected

enpres

effective number of presidential candidates

medmag

median district magnitude

newdem

first election of new democracy

proximity1

proximity - continuous

proximity2

proximity - dichotomous

seats

assembly size

upperseats

number of upper tier seats

uppertier

percentage of uppertier seats

uppertier_eneg

uppertier*eneg

logmag_eneg

logmag*eneg

proximity1_enpres

proximity1*enpres

twoelections

n/a

twoelections1

n/a

...

Source

Clark, William R., and Matt Golder. 2006. "Rehabilitating Duverger's Theory." Comparative Political Studies 39(6): 679-708.