In michaelpmcdonald/IATanalyses: Implements a variety of analyses of IAT data in support of a mechanistic investigation of the IAT effect

Presented here are drift diffusion model parameters generated from Race IAT data collected between February and August 2015. Parameters are estimated using the analytical "canonical" method.

Note: All error bars represent +/- 2 SE.

library(dplyr)
library(data.table)
library(bit64)
library(tidyr)
library(ggplot2)
library(xtable)
library(knitr)
library(pander)
setwd("..")
source("R/DDMfunctions.R")
source("R/summarySE.R")
source("scripts/performDDManalysis.R")

pander(select(DDMv, -ci))
ggplot(DDMv, aes(x=pairingType, y=v, fill=practiceOrTest)) +
  geom_bar(position=position_dodge(), stat="identity") +
  geom_errorbar(aes(ymin=v-2*se, ymax=v+2*se),
                width=.2,                    # Width of the error bars
                position=position_dodge(.9))
kable(DDMa)

ggplot(DDMa, aes(x=pairingType, y=a, fill=practiceOrTest)) +
  geom_bar(position=position_dodge(), stat="identity") +
  geom_errorbar(aes(ymin=a-2*se, ymax=a+2*se),
                width=.2,                    # Width of the error bars
                position=position_dodge(.9))
kable(DDMter)

ggplot(DDMter, aes(x=pairingType, y=ter, fill=practiceOrTest)) +
  geom_bar(position=position_dodge(), stat="identity") +
  geom_errorbar(aes(ymin=ter-2*se, ymax=ter+2*se),
                width=.2,                    # Width of the error bars
                position=position_dodge(.9))

michaelpmcdonald/IATanalyses documentation built on May 22, 2019, 9:52 p.m.