Nothing
Gscores <- function(IATlong)
{
# Comments in "quotes" from Nosek, Bar-Anan, Sriram, Greenwald (2013),
# "Understanding and using the brief implicit association test: I.
# recommended scoring procedures". Table 9. http://ssrn.com/abstract=2196002
Gaussianranks <- function(x)
{
# It handles NA values by leaving them in the same place as found
y <- x[!is.na(x)]
# "1. Assign fractional ranks to N latencies. The longest latency will be
# assigned a value of 1.0 and the shortest will be assigned a value of 1/N.
# In the case of ties, ranks are averaged across tied values"
N <- length(y)
Fr <- rank(y)/N
# "2. Subtract 1/2N from each fractional rank. Assuming untied values, the
# largest latency will now have a value of 1-1/2N or (2N-1)/2N.
# The 1/2N downward adjustment applies generally, even when tied values
# exist".
Fr <- Fr - 1/(2*N)
# "3. For each of the N observations, compute the standard normal deviate
# (mean = 0 and standard deviation = 1) corresponding to the adjusted
# fractinal rank latency".
Gr <- scale(Fr)
# Remove the attributes given by the scale() function
attr(Gr, "scaled:center") <- NULL
attr(Gr, "scaled:scale") <- NULL
x[!is.na(x)] <- Gr
x
}
# "4. G1 is the mean of the normal deviates in condition 1. G2 is the mean
# of the normal deviates in condition 2"
Mranks <- filter(IATlong, variable != "pxxxx") %>%
group_by(subject, variable) %>% # here do NOT group by blockcode
mutate(Gr = Gaussianranks(RT)) %>% # compute gaussian ranks
group_by(subject, variable, blockcode) %>% # here do group by blockcode
summarize(Mean = mean(Gr, na.rm = TRUE)) # mean rank in each block
Mranks <- dcast(Mranks, subject*variable ~ blockcode, value.var = "Mean")
# "5. G = G2-G1"
Mranks <- mutate(Mranks, Gscore = pair2 - pair1)
# Put data in wide format
Gsc <- dcast(Mranks, subject ~ variable, value.var = "Gscore")
names(Gsc) <- str_replace(names(Gsc), "xx", "2x")
Gsc
}
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